Controlling host vehicle based on a predicted state of a parked vehicle

ABSTRACT

Systems and methods are provided for navigating an autonomous vehicle. In one implementation, a system for determining a predicted state of a parked vehicle in an environment of a host vehicle may include an image capture device, an infrared image capture device, and at least one processing device. The processing device may be programmed to receive a plurality of images associated with the environment of the host vehicle, analyze at least one of the plurality of images to identify the parked vehicle, analyze at least two of the plurality of images to identify a change in an illumination state of at least one light associated with the parked vehicle, receive at least one thermal image of the parked vehicle, determine the predicted state of the parked vehicle, and cause at least one navigational response by the host vehicle based on the predicted state of the parked vehicle.

CROSS REFERENCES TO RELATED APPLICATIONS

This application claims the benefit of priority of U.S. ProvisionalPatent Application No. 62/354,946, filed on Jun. 27, 2016; and U.S.Provisional Patent Application No. 62/445,500, filed on Jan. 12, 2017.The foregoing applications are incorporated herein by reference in theirentirety.

BACKGROUND Technical Field

The present disclosure relates generally to autonomous vehiclenavigation. Additionally, this disclosure relates to systems and methodsfor navigating a host vehicle based on detecting door openings,navigating a host vehicle based on detecting a target vehicle enteringthe host vehicle's lane, navigating a host vehicle based on detectingwhether a road on which the host vehicle travels is a one-way road, anddetermining a predicted state of a parked vehicle.

Background Information

As technology continues to advance, the goal of a fully autonomousvehicle that is capable of navigating on roadways is on the horizon.Autonomous vehicles may need to take into account a variety of factorsand make appropriate decisions based on those factors to safely andaccurately reach an intended destination. For example, an autonomousvehicle may need to process and interpret visual information (e.g.,information captured from a camera), information from radar or lidar,and may also use information obtained from other sources (e.g., from aGPS device, a speed sensor, an accelerometer, a suspension sensor,etc.). At the same time, in order to navigate to a destination, anautonomous vehicle may also need to identify its location within aparticular roadway (e.g., a specific lane within a multi-lane road),navigate alongside other vehicles, avoid obstacles and pedestrians,observe traffic signals and signs, travel from one road to another roadat appropriate intersections or interchanges, and respond to any othersituation that occurs or develops during the vehicle's operation.

Autonomous vehicles must be able to react to changing circumstances withsufficient time to adjust a navigation path of the vehicle or to applythe brakes. Many traditional algorithms, such as those used in extantautonomous braking systems, do not have reaction times comparable tothose of humans. Accordingly, such algorithms are often better suitedfor use as a backup to human drivers rather than use in a fullyautonomous vehicle.

Moreover, characteristics of parked cars are often good indicators ofcharacteristics of a road. For example, the direction of the parked carsmay indicate whether the road is a one-way road, and the space betweenvehicles may indicate whether a pedestrian might emerge from between thevehicles. Existing autonomous vehicle algorithms, however, do not usesuch characteristics,

Finally, autonomous vehicle systems may use measurements to which humandrivers do not have access. For example, autonomous vehicle systems mayemploy infrared cameras to assess the environment and make predictions.However, many traditional systems do not utilize a combination ofmeasurements, such as visual and infrared cameras. Embodiments of thepresent disclosure may address one or more of the shortcomings oftraditional systems discussed above.

SUMMARY

Embodiments consistent with the present disclosure provide systems andmethods for autonomous vehicle navigation. The disclosed embodiments mayuse cameras to provide autonomous vehicle navigation features. Forexample, consistent with the disclosed embodiments, the disclosedsystems may include one, two, or more cameras that monitor theenvironment of a vehicle. The disclosed systems may provide anavigational response based on, for example, an analysis of imagescaptured by one or more of the cameras. Some embodiments may furtherinclude one, two, or more infrared cameras that monitor the environment.Some embodiments may thus provide a navigational response based on, forexample, analysis of visual images, infrared images, or any combinationthereof.

The navigational response may also take into account other dataincluding, for example, global positioning system (GPS) data, sensordata (e.g., from an accelerometer, a speed sensor, a suspension sensor,etc.), and/or other map data.

In one embodiment, a system for navigating a host vehicle based ondetecting a door opening event in an environment of the vehicle maycomprise at least one processing device. The at least one processingdevice may be programmed to receive, from an image capture device, atleast one image associated with the environment of the host vehicle andanalyze the at least one image to identify a side of a parked vehicle.The at least one processing device may be further programmed toidentify, in the at least one image, a first structural feature of theparked vehicle in a forward region of the side of the parked vehicle anda second structural feature of the parked vehicle in a rear region ofthe side of the parked vehicle and identify, in the at least one image,a door edge of the parked vehicle in a vicinity of the first and secondstructural features. The at least one processing device may also beprogrammed to determine, based on analysis of one or more subsequentimages received from the image capture device, a change of an imagecharacteristic of the door edge of the parked vehicle and alter anavigational path of the host vehicle based at least in part on thechange of the image characteristic of the door edge of the parkedvehicle.

In another embodiment, a method for navigating a host vehicle based ondetecting a door opening event in an environment of the vehicle maycomprise receiving, from an image capture device, at least one imageassociated with the environment of the host vehicle and analyzing the atleast one image to identify a side of a parked vehicle. The method mayfurther comprise identifying, in the at least one image, a firststructural feature of the parked vehicle in a forward region of the sideof the parked vehicle and a second structural feature of the parkedvehicle in a rear region of the side of the parked vehicle andidentifying, in the at least one image, a door edge of the parkedvehicle in a vicinity of the first and second structural features. Themethod may also comprise determining, based on analysis of one or moresubsequent images received from the image capture device, a change of animage characteristic of the door edge of the parked vehicle and alteringa navigational path of the host vehicle based at least in part on thechange of the image characteristic of the door edge of the parkedvehicle.

In yet another embodiment, a system for navigating a host vehicle basedon movement of a target vehicle toward a lane being traveled by the hostvehicle may comprise at least one processing device. The at least oneprocessing device may be programmed to receive, from an image capturedevice, a plurality of images associated with an environment of the hostvehicle and analyze at least one of the plurality of images to identifythe target vehicle and at least one wheel component on a side of thetarget vehicle. The at least one processing device may be furtherprogrammed to analyze, in at least two of the plurality of images, aregion including the at least one wheel component of the target vehicleto identify motion associated with the at least one wheel component ofthe target vehicle and cause at least one navigational change of thehost vehicle based on the identified motion associated with the at leastone wheel component of the target vehicle.

In yet another embodiment, a method for navigating a host vehicle basedon movement of a target vehicle toward a lane being traveled by the hostvehicle may comprise receiving, from an image capture device, aplurality of images associated with an environment of the host vehicleand analyzing at least one of the plurality of images to identify thetarget vehicle and at least one wheel component on a side of the targetvehicle. The method may further comprise analyzing, in at least two ofthe plurality of images, a region including the at least one wheelcomponent of the target vehicle to identify motion associated with theat least one wheel component of the target vehicle and causing at leastone navigational change of the host vehicle based on the identifiedmotion associated with the at least one wheel component of the targetvehicle.

In still another embodiment, a system for detecting whether a road onwhich a host vehicle travels is a one-way road may comprise at least oneprocessing device. The at least one processing device may be programmedto receive, from an image capture device, at least one image associatedwith an environment of the host vehicle; identify, based on analysis ofthe at least one image, a first plurality of vehicles on a first side ofthe road on which the host vehicle travels; and identify, based onanalysis of the at least one image, a second plurality of vehicles on asecond side of the road on which the host vehicle travels. The at leastone processing device may be further programmed to determine a firstfacing direction associated with the first plurality of vehicles;determine a second facing direction associated with the second pluralityof vehicles; and cause at least one navigational change of the hostvehicle when the first facing direction and the second facing directionare both opposite to a heading direction of the host vehicle.

In still another embodiment, a method for detecting whether a road onwhich a host vehicle travels is a one-way road may comprise receiving,from an image capture device, at least one image associated with anenvironment of the host vehicle; identifying, based on analysis of theat least one image, a first plurality of vehicles on a first side of theroad on which the host vehicle travels; and identifying, based onanalysis of the at least one image, a second plurality of vehicles on asecond side of the road on which the host vehicle travels. The methodmay further comprise determining a first facing direction associatedwith the first plurality of vehicles; determining a second facingdirection associated with the second plurality of vehicles; and causingat least one navigational change of the host vehicle when the firstfacing direction and the second facing direction are both opposite to aheading direction of the host vehicle.

In another embodiment, a system for navigating a host vehicle maycomprise at least one processing device. The at least one processingdevice may be programmed to receive a. navigation instruction tonavigate the host vehicle from a first road on which the host vehicle istraveling to a second road and receive, from an image capture device, atleast one image associated with an environment of the second road. Theat least one processing device may be further programmed to identify,based on analysis of the at least one image, a first plurality ofvehicles on a first side of the second road and identify, based onanalysis of the at least one image, a second plurality of vehicles on asecond side of the second road. The at least one processing device mayalso be programmed to determine a first facing direction associated withthe first plurality of vehicles, determine a second facing directionassociated with the second plurality of vehicles, and determine that thefirst facing direction and the second facing direction are both oppositeto a heading direction the host vehicle would travel if the host vehiclewere to turn onto the second road. The at least one processing devicemay be further programmed to suspend the navigation instruction inresponse to the determination that the first facing direction and thesecond facing direction are both opposite to the heading direction thehost vehicle would travel if the host vehicle were to navigate onto thesecond road.

In yet another embodiment, a system for determining a predicted state ofa parked vehicle in an environment of a host vehicle may comprise animage capture device, an infrared image capture device, and at least oneprocessing device. The at least one processing device may be programmedto receive, from the image capture device, a plurality of imagesassociated with the environment of the host vehicle; analyze at leastone of the plurality of images to identify the parked vehicle; andanalyze at least two of the plurality of images to identify a change inan illumination state of at least one light associated with the parkedvehicle. The at least one processing device may be further programmed toreceive, from the infrared image capture device, at least one thermalimage of the parked vehicle; determine, based on the change in theillumination state and analysis of the at least one thermal image, thepredicted state of the parked vehicle; and cause at least onenavigational response by the host vehicle based on the predicted stateof the parked vehicle.

In yet another embodiment, a method for determining a predicted state ofa parked vehicle in an environment of a host vehicle may comprisereceiving, from an image capture device, a plurality of imagesassociated with the environment of the host vehicle; analyzing at leastone of the plurality of images to identify the parked vehicle; andanalyzing at least two of the plurality of images to identify a changein an illumination state of at least one light associated with theparked vehicle. The method may further comprise receiving, from aninfrared image capture device, at least one thermal image of the parkedvehicle; determining, based on the change in the illumination state andanalysis of the at least one thermal image, the predicted state of theparked vehicle; and causing at least one navigational response by thehost vehicle based on the predicted state of the parked vehicle.

In yet another embodiment, a system for determining a predicted state ofa parked vehicle in an environment of a host vehicle may comprise animage capture device and at least one processing device. The at leastone processing device may be programmed to receive, from the imagecapture device, a plurality of images associated with the environment ofthe host vehicle. The at least one processing device may be furtherprogrammed to analyze at least one of the plurality of images toidentify the parked vehicle and analyze at least two of the plurality ofimages to identify a change in an illumination state of at least onelight associated with the parked vehicle. The at least one processingdevice may also be programmed to determine, based on the change in theillumination state, the predicted state of the parked vehicle and causeat least one navigational response by the host vehicle based on thepredicted stale of the parked vehicle

In still another embodiment, a system for navigating a host vehicle maycomprise at least one processing device. The at least one processingdevice may be programmed to receive, from a camera, a plurality ofimages representative of an environment of the host vehicle and analyzeat least one of the plurality of images to identify at least twostationary vehicles. The at least one processing device may be furtherprogrammed to determine, based on analysis of the at least of theplurality of images, a spacing between the two stationary vehicles andcause at least one navigational change in the host vehicle based on amagnitude of the spacing determined between the two stationary vehicles.

In still another embodiment, a method for navigating a host vehicle maycomprise receiving, from a camera, a plurality of images representativeof an environment of the host vehicle and analyzing at least one of theplurality of images to identify at least two stationary vehicles. Themethod may further comprise determining, based on analysis of the atleast of the plurality of images, a spacing between the two stationaryvehicles and causing at least one navigational change in the hostvehicle based on a magnitude of the spacing determined between the twostationary vehicles.

Consistent with other disclosed embodiments, non-transitorycomputer-readable storage media may store program instructions, whichare executed by at least one processing device and perform any of themethods described herein.

The foregoing general description and the following detailed descriptionare exemplary and explanatory only and are not restrictive of theclaims.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this disclosure, illustrate various disclosed embodiments. Inthe drawings:

FIG. 1 is a diagrammatic representation of an exemplary systemconsistent with the disclosed embodiments.

FIG. 2A is a diagrammatic side view representation of an exemplaryvehicle including a system consistent with the disclosed embodiments.

FIG. 2B is a diagrammatic top view representation of the vehicle andsystem shown in FIG. 2A consistent with the disclosed embodiments.

FIG. 2C is a diagrammatic top view representation of another embodimentof a vehicle including a system consistent with the disclosedembodiments.

FIG. 2D is a diagrammatic top view representation of yet anotherembodiment of a vehicle including a system consistent with the disclosedembodiments.

FIG. 2E is a diagrammatic top view representation of yet anotherembodiment of a vehicle including a system consistent with the disclosedembodiments.

FIG. 2F is a diagrammatic representation of exemplary vehicle controlsystems consistent with the disclosed embodiments.

FIG. 3A is a diagrammatic representation of an interior of a vehicleincluding a rearview mirror and a user interface for a vehicle imagingsystem consistent with the disclosed embodiments.

FIG. 3B is an illustration of an example of a camera mount that isconfigured to be positioned behind a rearview mirror and against avehicle windshield consistent with the disclosed embodiments.

FIG. 3C is an illustration of the camera mount shown in FIG. 3B from adifferent perspective consistent with the disclosed embodiments.

FIG. 3D is an illustration of an example of a camera mount that isconfigured to be positioned behind a rearview mirror and against avehicle windshield consistent with the disclosed embodiments.

FIG. 4 is an exemplary block diagram of a memory configured to storeinstructions for performing one or more operations consistent with thedisclosed embodiments.

FIG. 5A is a flowchart showing an exemplary process for causing one ormore navigational responses based on monocular image analysis consistentwith disclosed embodiments.

FIG. 5B is a flowchart showing an exemplary process for detecting one ormore vehicles and/or pedestrians in a set of images consistent with thedisclosed embodiments.

FIG. 5C is a flowchart showing an exemplary process for detecting roadmarks and/or lane geometry information in a set of images consistentwith the disclosed embodiments.

FIG. 5D is a flowchart showing an exemplary process for detectingtraffic lights in a set of images consistent with the disclosedembodiments.

FIG. 5F is a flowchart showing an exemplary process for causing one ormore navigational responses based on a vehicle path consistent with thedisclosed embodiments.

FIG. 5F is a flowchart showing an exemplary process for determiningwhether a leading vehicle is changing lanes consistent with thedisclosed embodiments.

FIG. 6 is a flowchart showing an exemplary process for causing one ormore navigational responses based on stereo image analysis consistentwith the disclosed embodiments.

FIG. 7 is a flowchart showing an exemplary process for causing one ormore navigational responses based on an analysis of three sets of imagesconsistent with the disclosed embodiments.

FIG. 8 is another exemplary block diagram of a memory configured tostore instructions for performing one or more operations consistent withthe disclosed embodiments.

FIG. 9 is a schematic view of a road from a point-of-view of a systemconsistent with disclosed embodiments.

FIG. 10 is another schematic view of a road from a point-of-view of asystem consistent with the disclosed embodiments.

FIG. 11 is a schematic view of a parked car from a point-of-view of asystem consistent with the disclosed embodiments.

FIG. 12A is a schematic view of a door opening event from apoint-of-view of a system consistent with the disclosed embodiments.

FIG. 12B is another schematic view of a door opening event from apoint-of-view of a system consistent with the disclosed embodiments.

FIG. 13 is another schematic view of a road from a point-of-view of asystem consistent with the disclosed embodiments.

FIG. 14 is a flowchart showing an exemplary process for causing one ormore navigational responses based on detection of a door opening eventconsistent with the disclosed embodiments.

FIG. 15 is another exemplary block diagram of a memory configured tostore instructions for performing one or more operations consistent withthe disclosed embodiments.

FIG. 16A is a schematic view of a parked car from a point-of-view of asystem consistent with the disclosed embodiments.

FIG. 16B is another schematic view of a parked car from a point-of-viewof a system consistent with the disclosed embodiments.

FIG. 17 is a flowchart showing an exemplary process for causing one ormore navigational responses based on detection of a target vehicleentering the host vehicle's lane consistent with the disclosedembodiments.

FIG. 18 is a flowchart showing an exemplary process for warping thehomography of a road consistent with the disclosed embodiments.

FIG. 19 is another exemplary block diagram of a memory configured tostore instructions for performing one or more operations consistent withthe disclosed embodiments.

FIG. 20A is a schematic view of a one-way road from a point-of-view of asystem consistent with the disclosed embodiments.

FIG. 20B is another schematic view of a one-way road from apoint-of-view of a system consistent with the disclosed embodiments.

FIG. 21 is a flowchart showing an exemplary process for causing one ormore navigational responses based on detection of whether a road onwhich the host vehicle travels is a one-way road consistent with thedisclosed embodiments.

FIG. 22 is another exemplary block diagram of a memory configured tostore instructions for performing one or more operations consistent withthe disclosed embodiments.

FIG. 23A is a schematic view of a parked car from a point-of-view of asystem consistent with the disclosed embodiments.

FIG. 23B is a schematic view of a parked car having a change inillumination from a point-of-view of a system consistent with thedisclosed embodiments.

FIG. 23C is a schematic view of a heat map of a parked car from apoint-of-view of a system consistent with the disclosed embodiments.

FIG. 24 is a flowchart showing an exemplary process for determining apredicted state of a parked vehicle consistent with the disclosedembodiments.

FIG. 25 is a flowchart showing an exemplary process for aligning visualand infrared images from a system consistent with the disclosedembodiments.

FIG. 26 is another exemplary block diagram of a memory configured tostore instructions for performing one or more operations consistent withthe disclosed embodiments.

FIG. 27A is another schematic view of a road from a point-of-view of asystem consistent with the disclosed embodiments.

FIG. 27B is another schematic view of a road having a detection hot spotconsistent with the disclosed embodiments.

FIG. 28 is a flowchart showing an exemplary process for navigating ahost vehicle consistent with the disclosed embodiments.

DETAILED DESCRIPTION

The following detailed description refers to the accompanying drawings.Wherever possible, the same reference numbers are used in the drawingsand the following description to refer to the same or similar parts.While several illustrative embodiments are described herein,modifications, adaptations and other implementations are possible. Forexample, substitutions, additions or modifications may be made to thecomponents illustrated in the drawings, and the illustrative methodsdescribed herein may be modified by substituting, reordering, removing,or adding steps to the disclosed methods. Accordingly, the followingdetailed description is not limited to the disclosed embodiments andexamples. Instead, the proper scope is defined by the appended claims.

Autonomous Vehicle Overview

As used throughout this disclosure, the term “autonomous vehicle” refersto a vehicle capable of implementing at least one navigational changewithout driver input. A “navigational change” refers to a change in oneor more of steering, braking, or acceleration/deceleration of thevehicle. To be autonomous, a vehicle need not be fully automatic (e.g.,fully operational without a driver or without driver input). Rather, anautonomous vehicle includes those that can operate under driver controlduring certain time periods and without driver control during other timeperiods. Autonomous vehicles may also include vehicles that control onlysome aspects of vehicle navigation, such as steering (e.g., to maintaina vehicle course between vehicle lane constraints) or some steeringoperations under certain circumstances (but not under allcircumstances), but may leave other aspects to the driver (e.g., brakingor braking under certain circumstances). In some cases, autonomousvehicles may handle some or all aspects of braking, speed control,and/or steering of the vehicle.

As human drivers typically rely on visual cues and observations in orderto control a vehicle, transportation infrastructures are builtaccordingly, with lane markings, traffic signs, and traffic lightsdesigned to provide visual information to drivers. In view of thesedesign characteristics of transportation infrastructures, an autonomousvehicle may include a camera and a processing unit that analyzes visualinformation captured from the environment of the vehicle. The visualinformation may include, for example, images representing components ofthe transportation infrastructure (e.g., lane markings, traffic signs,traffic lights, etc.) that are observable by drivers and other obstacles(e.g., other vehicles, pedestrians, debris, etc.), The autonomousvehicle may also include an infrared camera. In such embodiments, theprocessing unit may analyze heat information captured from theenvironment, either individually or in conjunction with visualinformation.

Additionally, an autonomous vehicle may also use stored information,such as information that provides a model of the vehicle's environmentwhen navigating. example, the vehicle may use GPS data, sensor data(e.g., from an accelerometer, a speed sensor, a suspension sensor,etc.), and/or other map data to provide information related to itsenvironment while it is traveling, and the vehicle (as well as othervehicles) may use the information to localize itself on the model. Somevehicles can also be capable of communication among them, sharinginformation, altering the peer vehicle of hazards or changes in thevehicles' surroundings, etc.

System Overview

FIG. 1 is a block diagram representation of a system 100 consistent withthe exemplary disclosed embodiments. System 100 may include variouscomponents depending on the requirements of a particular implementation.In some embodiments, system 100 may include a processing unit 110, animage acquisition unit 120, a position sensor 130, one or more memoryunits 140, 150, a map database 160, a user interface 170, and a wirelesstransceiver 172. Processing unit 110 may include one or more processingdevices. In some embodiments, processing unit 110 may include anapplications processor 180, an image processor 190, or any othersuitable processing device. Similarly, image acquisition unit 120 mayinclude any number of image acquisition devices and components dependingon the requirements of a particular application. some embodiments, imageacquisition unit 120 may include one or more image capture devices(e.g., cameras, CCDs, or any other type of image sensor), such as imagecapture device 122, image capture device 124, and image capture device126. In some embodiments, image acquisition unit 120 may further includeone or more infrared capture devices (e.g., infrared cameras, farinfrared (FIR) detectors, or any other type of infrared sensor); forexample, one or more of image capture device 122, image capture device124, and image capture device 126 may comprise an infrared image capturedevice.

System 100 may also include a data interface 128 communicativelyconnecting processing unit 110 to image acquisition unit 120. Forexample, data interface 128 may include any wired and/or wireless linkor links for transmitting image data acquired by image acquisition unit120 to processing unit 110.

Wireless transceiver 172 may include one or more devices configured toexchange transmissions over an air interface to one or more networks(e.g., cellular, the Internet, etc.) by use of a radio frequency,infrared frequency, magnetic field, or an electric field. Wirelesstransceiver 172 may use any known standard to transmit and/or receivedata (e.g., Wi-Fi, Bluetooth®, Bluetooth Smart, 802.15.4, ZigBee, etc.).Such transmissions can include communications from the host vehicle toone or more remotely located servers. Such transmissions may alsoinclude communications (one-way or two-way) between the host vehicle andone or more target vehicles in an environment of the host vehicle (e.g.,to facilitate coordination of navigation of the host vehicle in view ofor together with target vehicles in the environment of the hostvehicle), or even a broadcast transmission to unspecified recipients ina vicinity of the transmitting vehicle.

Both applications processor 180 and image processor 190 may includevarious types of hardware-based processing devices. For example, eitheror both of applications processor 180 and image processor 190 mayinclude a microprocessor, preprocessors (such as an image preprocessor),graphics processors, a central processing unit (CPU), support circuits,digital signal processors, integrated circuits, memory, graphicsprocessing unit (GPU), or any other types of devices suitable forrunning applications and for image processing and analysis. In someembodiments, applications processor 180 and/or image processor 190 mayinclude any type of single or multi-core processor, mobile devicemicrocontroller, central processing unit, etc. Various processingdevices may be used, including, for example, processors available frommanufacturers such as Intel®, AMD®, etc., or GPUs available frommanufacturers such as NVIDIA®, ATI®, etc. and may include variousarchitectures (e.g., x86 processor, ARM®, etc.).

In some embodiments, applications processor 180 and/or image processor190 may include any of the EyeQ series of processor chips available fromMobileye®. These processor designs each include multiple processingunits with local memory and instruction sets. Such processors mayinclude video inputs for receiving image data from multiple imagesensors and may also include video out capabilities. In one example, theEyeQ2® uses 90 nm-micron technology operating at 332 Mhz. The EyeQ2®architecture consists of two floating point, hyper-thread 32-bit RISCCPUs (MIPS32® 34K® cores), five Vision Computing Engines (VCE), threeVector Microcode Processors (VMP®), Denali 64-bit Mobile DDR Controller,128-bit internal Sonics Interconnect, dual 16-bit Video input and 18-bitVideo output controllers, 16 channels DMA and several peripherals. TheMIPS34K CPU manages the five VCEs, three VMP™ and the DMA, the secondMIPS34K CPU and the multi-channel DMA as well as the other peripherals.The five VCEs, three VMP® and the MIPS34K CPU can perform intensivevision computations required by multi-function bundle applications. Inanother example, the EyeQ3®, which is a third generation processor andis six times more powerful that the EyeQ2®, may be used in the disclosedembodiments. In other examples, the EyeQ4® and/or the EyeQ5® may be usedin the disclosed embodiments, Of course, any newer or future EyeQprocessing devices may also be used together with the disclosedembodiments.

Any of the processing devices disclosed herein may be configured toperform certain functions. Configuring a processing device, such as anyof the described EyeQ processors or other controller or microprocessor,to perform certain functions may include programming of computerexecutable instructions and making those instructions available to theprocessing device for execution during operation of the processingdevice. In some embodiments, configuring a processing device may includeprogramming the processing device directly with architecturalinstructions. For example, processing devices such as field-programmablegate arrays (FPGAs), application-specific integrated circuits (ASICs),and the like may be configured using, for example, one or more hardwaredescription languages (HDLs).

In other embodiments, configuring a processing device may includestoring executable instructions on a memory that is accessible to theprocessing device during operation. For example, the processing devicemay access the memory to obtain and execute the stored instructionsduring operation. In either case, the processing device configured toperform the sensing, image analysis, and/or navigational functionsdisclosed herein represents a specialized hardware-based system incontrol of multiple hardware based components of a host vehicle.

While FIG. 1 depicts two separate processing devices included inprocessing unit 110, more or fewer processing devices may be used. Forexample, in some embodiments, a single processing device may be used toaccomplish the tasks of applications processor 180 and image processor190. In other embodiments, these tasks may be performed by more than twoprocessing devices. Further, in some embodiments, system 100 may includeone or more of processing unit 110 without including other components,such as image acquisition unit 120.

Processing unit 110 may comprise various types of devices. For example,processing unit 110 may include various devices, such as a controller,an image preprocessor, a central processing unit (CPU), a graphicsprocessing unit (GPU), support circuits, digital signal processors,integrated circuits, memory, or any other types of devices for imageprocessing and analysis. The image preprocessor may include a videoprocessor for capturing, digitizing and processing the imagery from theimage sensors. The CPU may comprise any number of microcontrollers ormicroprocessors. The GPU may also comprise any number ofmicrocontrollers or microprocessors. The support circuits may be anynumber of circuits generally well known in the art, including cache,power supply, clock and input-output circuits. The memory may storesoftware that, when executed by the processor, controls the operation ofthe system. The memory may include databases and image processingsoftware. The memory may comprise any number of random access memories,read only memories, flash memories, disk drives, optical storage, tapestorage, removable storage and other types of storage. In one instance,the memory may be separate from the processing unit 110. In anotherinstance, the memory may be integrated into the processing unit 110.

Each memory 140, 150 may include software instructions that whenexecuted by a processor (e.g., applications processor 180 and/or imageprocessor 190), may control operation of various aspects of system 100.These memory units may include various databases and image processingsoftware, as well as a trained system, such as a neural network, or adeep neural network, for example. The memory units may include randomaccess memory (RAM), read only memory (ROM), flash memory, disk drives,optical storage, tape storage, removable storage and/or any other typesof storage. In some embodiments, memory units 140, 150 may be separatefrom the applications processor 180 and/or image processor 190. In otherembodiments, these memory units may be integrated into applicationsprocessor 180 and/or image processor 190.

Position sensor 130 may include any type of device suitable fordetermining a location associated with at least one component of system100. In some embodiments, position sensor 130 may include a GPSreceiver. Such receivers can determine a user position and velocity byprocessing signals broadcasted by global positioning system satellites.Position information from position sensor 130 may be made available toapplications processor 180 and/or image processor 190.

In some embodiments, system 100 may include components such as a speedsensor (e.g., a speedometer) for measuring a speed of vehicle 200.System 100 may also include one or more accelerometers (either singleaxis or multiaxis) for measuring accelerations of vehicle 200 along oneor more axes.

The memory units 140, 150 may include a database, or data organized inany other form, that includes one or more indicators and/or locations ofknown landmarks. Sensory information (such as images, radar signal,depth information from lidar or stereo processing of two or more images)of the environment may be processed together with position information,such as a GPS coordinate, vehicle's ego motion, etc. to determine acurrent location of the vehicle relative to the known landmarks, andrefine the vehicle location. Certain aspects of this technology areincluded in a localization technology known as REM™, which is beingmarketed by the assignee of the present application.

User interface 170 may include any device suitable for providinginformation to or for receiving inputs from one or more users of system100. In some embodiments, user interface 170 may include user inputdevices, including, for example, a touchscreen, microphone, keyboard,pointer devices, track wheels, cameras, knobs, buttons, etc. With suchinput devices, a user may be able to provide information inputs orcommands to system 100 by typing instructions or information, providingvoice commands, selecting menu options on a screen using buttons,pointers, or eye-tracking capabilities, or through any other suitabletechniques for communicating information to system 100.

User interface 170 may be equipped with one or more processing devicesconfigured to provide and receive information to or from a user andprocess that information for use by, for example, applications processor180. In some embodiments, such processing devices may executeinstructions for recognizing and tracking eye movements, receiving andinterpreting voice commands, recognizing and interpreting touches and/orgestures made on a touchscreen, responding to keyboard entries or menuselections, etc. In some embodiments, user interface 170 may include adisplay, speaker, tactile device, and/or any other devices for providingoutput information to a user.

Map database 160 may include any type of database for storing map datauseful to system 100. In some embodiments, map database 160 may includedata relating to the position, in a reference coordinate system, ofvarious items, including roads, water features, geographic features,businesses, points of interest, restaurants, gas stations, etc. Mapdatabase 160 may store not only the locations of such items, but alsodescriptors relating to those items, including, for example, namesassociated with any of the stored features. In some embodiments, mapdatabase 160 may be physically located with other components of system100. Alternatively or additionally, map database 160 or a portionthereof may be located remotely with respect to other components ofsystem 100 (e.g., processing unit 110). In such embodiments, informationfrom map database 160 may be downloaded over a wired or wireless dataconnection to a network (e.g., over a cellular network and/or theInternet, etc.). in some cases, map database 160 may store a sparse datamodel including polynomial representations of certain road features lanemarkings) or target trajectories for the host vehicle. Map database 160may also include stored representations of various recognized landmarksthat may be used to determine or update a known position of the hostvehicle with respect to a target trajectory. The landmarkrepresentations may include data fields such as landmark type, landmarklocation, among other potential identifiers.

Image capture devices 122, 124, and 126 may each include any type ofdevice suitable for capturing at least one image from an environment.Moreover, any number of image capture devices may he used to acquireimages for input to the image processor. Some embodiments may includeonly a single image capture device, while other embodiments may includetwo, three, or even four or more image capture devices.

Furthermore, as explained above, image capture devices 122, 124, and 126may each include any type of device suitable for capturing at least oneinfrared image from an environment. Any number of infrared image capturedevices may be used. Some embodiments may include only a single infraredimage capture device, while other embodiments may include two, three, oreven four or more infrared image capture devices. Moreover, someembodiments may include any number of infrared image capture devices incombination with any number of image capture devices. Image capturedevices 122, 124, and 126 will be further described with reference toFIGS. 2B-2E, below.

One or more cameras image capture devices 122, 124, and 126) may be partof a sensing block included on a vehicle. The sensing block may furtherinclude one or more infrared image cameras, either separately or incombination with one or more cameras.

Various other sensors may be included in the sensing block, and any orall of the sensors may be relied upon to develop a sensed navigationalstate of the vehicle. In addition to cameras (forward, sideward,rearward, etc.), other sensors such as RADAR, LIDAR, and acousticsensors may be included in the sensing block. Additionally, the sensingblock may include one or more components configured to communicate andtransmit/receive information relating to the environment of the vehicle.For example, such components may include wireless transceivers (RF, etc)that may receive from a source remotely located with respect to the hostvehicle sensor based information or any other type of informationrelating to the environment of the host vehicle. Such information mayinclude sensor output information, or related information, received fromvehicle systems other than the host vehicle. In some embodiments, suchinformation may include information received from a remote computingdevice, a centralized server, etc. Furthermore, the cameras may take onmany different configurations: single camera units, multiple cameras,camera clusters, long FOV, short FOV, wide angle, fisheye, etc.

System 100, or various components thereof, may be incorporated intovarious different platforms. In some embodiments, system 100 may beincluded on a vehicle 200, as shown in FIG. 2A. For example, vehicle 200may be equipped with a processing unit 110 and any of the othercomponents of system 100, as described above relative to FIG. 1. Whilein some embodiments vehicle 200 may be equipped with only a single imagecapture device (e.g., camera) and/or a single infrared image capturedevice, in other embodiments, such as those discussed in connection withFIGS. 2B-2E, multiple image capture devices and/or multiple infraredcapture devices may be used. For example, either of image capturedevices 122 and 124 of vehicle 200, as shown in FIG. 2A, may be part ofan ALIAS (Advanced Driver Assistance Systems) imaging set.

The image capture devices included on vehicle 200 as part of the imageacquisition unit 120 may be positioned at any suitable location. In someembodiments, as shown in FIGS. 2A-2E and 3A-3C, image capture device 122may be located in the vicinity of the rearview mirror. This position mayprovide a line of sight similar to that of the driver of vehicle 200,which may aid in determining what is and is not visible to the driver.Image capture device 122 may be positioned at any location near therearview mirror, but placing image capture device 122 on the driver sideof the mirror may further aid in obtaining images representative of thedriver's field of view and/or line of sight.

Other locations for the image capture devices of image acquisition unit120 may also be used. For example, image capture device 124 may belocated on or in a bumper of vehicle 200. Such a location may beespecially suitable for image capture devices having a wide field ofview. The line of sight of bumper-located image capture devices can bedifferent from that of the driver and, therefore, the bumper imagecapture device and driver may not always see the same objects. The imagecapture devices (e.g., image capture devices 122, 124, and 126) may alsobe located in other locations. For example, the image capture devicesmay be located on or in one or both of the side mirrors of vehicle 200,on the roof of vehicle 200, on the hood of vehicle 200, on the trunk ofvehicle 200, on the sides of vehicle 200, mounted on, positioned behind,or positioned in front of any of the windows of vehicle 200, and mountedin or near light fixtures on the front and/or back of vehicle 200, etc.

In addition to image capture devices, vehicle 200 may include variousother components of system 100. For example, processing unit 110 may beincluded on vehicle 200 either integrated with or separate from anengine control unit (ECU) of the vehicle. Vehicle 200 may also beequipped with a position sensor 130, such as a GPS receiver and may alsoinclude a map database 160 and memory units 140 and 150.

As discussed earlier, wireless transceiver 172 may transmit and/orreceive data over one or more networks (e.g., cellular networks, theInternet, etc.). For example, wireless transceiver 172. may upload datacollected by system 100 to one or more servers, and download data fromthe one or more servers. Via wireless transceiver 172, system 100 mayreceive, for example, periodic or on demand updates to data stored inmap database 160, memory 140, and/or memory 150. Similarly, wirelesstransceiver 172 may upload any data (e.g., images captured by imageacquisition unit 120, data received by position sensor 130 or othersensors, vehicle control systems, etc.) from system 100 and/or any dataprocessed by processing unit 110 to the one or more servers.

System 100 may upload data to a server (e.g., to the cloud) based on aprivacy level setting. For example, system 100 may implement privacylevel settings to regulate or limit the types of data (includingmetadata) sent to the server that may uniquely identify a vehicle and ordriver/owner of a vehicle. Such settings may be set by user via, forexample, wireless transceiver 172, be initialized by factory defaultsettings, or by data received by wireless transceiver 172.

In some embodiments, system 100 may upload data according to a “high”privacy level, and under such a setting, system 100 may transmit data(e.g., location information related to a route, captured images, etc.)without any details about the specific vehicle and/or driver/owner. Forexample, when uploading data according to a “high” privacy setting,system 100 may not include a vehicle identification number (VIN) or aname of a driver or owner of the vehicle, and may instead transmit data,such as captured images and/or limited location information related to aroute.

Other privacy levels are contemplated as well. For example, system 100may transmit data to a server according to an “intermediate” privacylevel and include additional information not included under a “high”privacy level, such as a make and/or model of a vehicle and/or a vehicletype (e.g., a passenger vehicle, sport utility vehicle, truck, etc.). Insome embodiments, system 100 may upload data according to a “low”privacy level. Under a “low” privacy level setting, system 100 mayupload data and include information sufficient to uniquely identify aspecific vehicle, owner/driver, and/or a portion or entirely of a routetraveled by the vehicle. Such “low” privacy level data may include oneor more of, for example, a VIN, a driver/owner name, an originationpoint of a vehicle prior to departure, an intended destination of thevehicle, a make and/or model of the vehicle, a type of the vehicle, etc.

FIG. 2A is a diagrammatic side view representation of an exemplaryvehicle imaging system consistent with the disclosed embodiments. FIG.2B is a diagrammatic top view illustration of the embodiment shown inFIG. 2A. As illustrated in FIG. 2B, the disclosed embodiments mayinclude a vehicle 200 including in its body a system 100 with a firstimage capture device 122 positioned in the vicinity of the rearviewmirror and/or near the driver of vehicle 200, a second image capturedevice 124 positioned on or in a bumper region (e.g., one of bumperregions 210) of vehicle 200, and a processing unit 110. In FIGS. 2A and2B, one or more of first image capture device 122 and second imagecapture device 124 may comprise an infrared image capture device.

As illustrated in FIG. 2C, image capture devices 122 and 124 may both bepositioned in the vicinity of the rearview mirror and/or near the driverof vehicle 200. In FIG. 2C, as in FIGS. 2A and 2B, one or more of firstimage capture device 122 and second image capture device 124 maycomprise an infrared image capture device.

Additionally, while two image capture devices 122 and 124 are shown inFIGS. 2B and 2C, it should be understood that other embodiments mayinclude more than two image capture devices. For example, in theembodiments shown in FIGS. 2D and 2E, first, second, and third imagecapture devices 122, 124, and 126, are included in the system 100 ofvehicle 200. Similar to FIGS. 2A, 2B, and 2C, one or more of first,second, and third image capture devices 122, 124, and 126 in FIGS. 2Dand 2E may comprise an infrared image capture device.

As illustrated in FIG. 2D, image capture device 122 may be positioned inthe vicinity of the rearview mirror and/or near the driver of vehicle200, and image capture devices 124 and 126 may be positioned on or in abumper region (e.g., one of bumper regions 210) of vehicle 200. And asshown in FIG. 2E, image capture devices 122, 124, and 126 may bepositioned in the vicinity of the rearview mirror and/or near the driverseat of vehicle 200. The disclosed embodiments are not limited to anyparticular number and configuration of the image capture devices, andthe image capture devices may be positioned in any appropriate locationwithin and/or on vehicle 200.

It is to be understood that the disclosed embodiments are not limited tovehicles and could be applied in other contexts. It is also to beunderstood that disclosed embodiments are not limited to a particulartype of vehicle 200 and may be applicable to all types of vehiclesincluding automobiles, trucks, trailers, and other types of vehicles.

The first image capture device 122 may include any suitable type ofimage capture device or infrared image capture device. Image capturedevice 122 may include an optical axis. In one instance, the imagecapture device 122 may include an AptinaM9V024 WVGA sensor with a globalshutter. In other embodiments, image capture device 122 may provide aresolution of 1280×960 pixels and may include a rolling shutter. Imagecapture device 122 may include various optical elements. In someembodiments, one or more lenses may be included, for example, to providea desired focal length and field of view for the image capture device.In some embodiments, image capture device 122 may be associated with a 6mm lens or a 12 mm lens. In some embodiments, image capture device 122may be configured to capture images having a desired field-of-view (FOV)202, as illustrated in FIG. 2D. For example, image capture device 122may be configured to have a regular FOV, such as within a range of 40degrees to 56 degrees, including a 46 degree FOV, 50 degree FOV, 52degree FOV, or greater. Alternatively, image capture device 122 may beconfigured to have a narrow FOV in the range of 23 to 40 degrees, suchas a 28 degree FOV or 36 degree FOV. In addition, image capture device122 may be configured to have a wide FOV in the range of 100 to 180degrees. In some embodiments, image capture device 122 may include awide angle bumper camera or one with up to a 180 degree FOV. In someembodiments, image capture device 122 may be a 7.2 M pixel image capturedevice with an aspect ratio of about 2:1 (e.g., H×V=3800×1900 pixels)with about 100 degree horizontal FOV. Such an image capture device maybe used in place of a three image capture device configuration. Due tosignificant lens distortion, the vertical FOV of such an image capturedevice may be significantly less than 50 degrees in implementations inwhich the image capture device uses a radially symmetric lens. Forexample, such a lens may not be radially symmetric which would allow fora vertical FOV greater than 50 degrees with 100 degree horizontal FOV.

The first image capture device 122 may acquire a plurality of firstimages relative to a scene associated with vehicle 200. Each of theplurality of first images may be acquired as a series of image scanlines, which may be captured using a rolling shutter. Each scan line mayinclude a plurality of pixels. In embodiments in which first imagecapture device 122 comprises an infrared image capture device, each ofthe plurality of first images may be acquired as a series of image scanlines, which may be captured using an electronic scanning system.

The first image capture device 122 may have a scan rate associated withacquisition of each of the first series of image scan lines. The scanrate may refer to a rate at which an image sensor can acquire image dataassociated with each pixel included in a particular scan line. Inembodiments in which first image capture device 122 comprises aninfrared image capture device, the scan rate may refer to a rate atwhich the infrared image sensor can acquire heat data associated witheach pixel included in a particular scan line.

Image capture devices 122, 124, and 126 may contain any suitable typeand number of image sensors, including CCD sensors or CMOS sensors, forexample. In one embodiment, a CMOS image sensor may be employed alongwith a rolling shutter, such that each pixel in a row is read one at atime, and scanning of the rows proceeds on a row-by-row basis until anentire image frame has been captured. In some embodiments, the rows maybe captured sequentially from top to bottom relative to the frame. Inembodiments in which one or more of image capture devices 122, 124, and126 comprises an infrared image capture device, an uncooled focal planearray (UFPA) may be employed along with an electronic scanning system,such that scanning of the rows proceeds on a row-by-row basis until anentire heat map has been captured.

In some embodiments, one or more of the image capture devices (e.g.,image capture devices 122, 124, and 126) disclosed herein may constitutea high resolution imager and may have a resolution greater than 5 Mpixel, 7 M pixel, 10 M pixel, or greater.

The use of a rolling shutter may result in pixels in different rowsbeing exposed and captured at different times, which may cause skew andother image artifacts in the captured image frame. On the other hand,when the image capture device 122 is configured to operate with a globalor synchronous shutter, all of the pixels may be exposed for the sameamount of time and during a common exposure period. As a result, theimage data in a frame collected from a system employing a global shutterrepresents a snapshot of the entire FOV (such as FOV 202) at aparticular time. In contrast, in a rolling shutter application, each rowin a frame is exposed and data is capture at different times. Thus,moving objects may appear distorted in an image capture device having arolling shutter. This phenomenon (which similarly applies to the use ofelectronic scanning in an infrared image capture device) will bedescribed in greater detail below.

The second image capture device 124 and the third image capturing device126 may be any type of image capture device or infrared image capturedevice. Like the first image capture device 122, each of image capturedevices 124 and 126 may include an optical axis. In one embodiment, eachof image capture devices 124 and 126 may include an Aptina M9V024 WVGAsensor with a global shutter. Alternatively, each of image capturedevices 124 and 126 may include a rolling shutter. Like image capturedevice 122, image capture devices 124 and 126 may be configured toinclude various lenses and optical elements. In some embodiments, lensesassociated with image capture devices 124 and 126 may provide FOVs (suchas FOVs 204 and 206) that are the same as, or narrower than, a FOV (suchas FOV 202) associated with image capture device 122. For example, imagecapture devices 124 and 126 may have FOVs of 40 degrees, 30 degrees, 26degrees, 23 degrees, 20 degrees, or less.

Image capture devices 124 and 126 may acquire a plurality of second andthird images relative to a scene associated with vehicle 200. Each ofthe plurality of second and third images may be acquired as a second andthird series of image scan lines, which may be captured using a rollingshutter. Each scan line or row may have a plurality of pixels. Imagecapture devices 124 and 126 may have second and third scan ratesassociated with acquisition of each of image scan lines included in thesecond and third series. In embodiments in which one or more of imagecapture devices 124 and 126 comprises an infrared image capture device,each of the plurality of second and third images may be acquired as asecond and third series of heat scan lines, which may be captured usingan electronic scanning system. In such embodiments, each scan line orrow may have a plurality of pixels, and image capture devices 124 and/or126 may have second and third scan rates associated with acquisition ofeach of heat scan lines included in the second and third series.

Each image capture device 122, 124, and 126 may be positioned at anysuitable position and orientation relative to vehicle 200. The relativepositioning of the image capture devices 122, 124, and 126 may beselected to aid in fusing together the information acquired from theimage capture devices. For example, in some embodiments, a FOV (such asFOV 204) associated with image capture device 124 may overlap partiallyor fully with a FOV (such as FOV 202) associated with image capturedevice 122 and a FOV (such as FOV 206) associated with image capturedevice 126.

Image capture devices 122, 124, and 126 may be located on vehicle 200 atany suitable relative heights. In one instance, there may be a heightdifference between the image capture devices 122, 124, and 126, whichmay provide sufficient parallax information to enable stereo analysis.For example, as shown in FIG. 2A, the two image capture devices 122 and124 are at different heights. There may also be a lateral displacementdifference between image capture devices 122, 124, and 126, givingadditional parallax information for stereo analysis by processing unit110, for example. The difference in the lateral displacement may bedenoted by d, as shown in FIGS. 2C and 2D. In some embodiments, fore oraft displacement (e.g., range displacement) may exist between imagecapture devices 122, 124, and 126. For example, image capture device 122may be located 0.5 to 2 meters or more behind image capture device 124and/or image capture device 126. This type of displacement may enableone of the image capture devices to cover potential blind spots of theother image capture device(s).

Similarly, there may be no height difference between the image capturedevices 122, 124, and 126, which may assist with aligning a heat mapproduced by one or more of the image capture devices with a visual imageproduced by one or more of the image capture devices.

Image capture devices 122 may have any suitable resolution capability(e.g., number of pixels associated with the image sensor), and theresolution of the image sensor(s) associated with the image capturedevice 122 may be higher, lower, or the same as the resolution of theimage sensor(s) associated with image capture devices 124 and 126. Insome embodiments, the image sensor(s) associated with image capturedevice 122 and/or image capture devices 124 and 126 may have aresolution of 640×480, 1024×768, 1280×960, or any other suitableresolution.

The frame rate (e.g., the rate at which an image capture device acquiresa set of pixel data or heat data of one image frame before moving on tocapture pixel data or heat data associated with the next image frame)may be controllable. The frame rate associated with image capture device122 may be higher, lower, or the same as the frame rate associated withimage capture devices 124 and 126. The frame rate associated with imagecapture devices 122, 124, and 126 may depend on a variety of factorsthat may affect the timing of the frame rate. For example, one or moreof image capture devices 122, 124, and 126 may include a selectablepixel delay period imposed before or after acquisition of image dataassociated with one or more pixels of an image sensor in image capturedevice 122, 124, and/or 126. Generally, image data corresponding to eachpixel may be acquired according to a clock rate for the device (e.g.,one pixel per clock cycle). Additionally, in embodiments including arolling shutter, one or more of image capture devices 122, 124, and 126may include a selectable horizontal blanking period imposed before orafter acquisition of image data associated with a row of pixels of animage sensor in image capture device 122, 124, and/or 126. Further, oneor more of image capture devices 122, 124, and/or 126 may include aselectable vertical blanking period imposed before or after acquisitionof image data associated with an image frame of image capture device122, 124, and 126. Similarly, in embodiments including electronicscanning, one or more of image capture devices 122, 124, and 126 mayinclude a dynamically variable scan rate.

These timing controls may enable synchronization of frame ratesassociated with image capture devices 122, 124, and 126, even where theline scan rates of each are different. Additionally, as will bediscussed in greater detail below, these selectable timing controls,among other factors (e.g., image sensor resolution, maximum line scanrates, etc.) may enable synchronization of image capture from an areawhere the FOV of image capture device 122. overlaps with one or moreFOVs of image capture devices 124 and 126, even where the field of viewof image capture device 122 is different from the FOVs of image capturedevices 124 and 126.

Frame rate timing in image capture device 122, 124, and 126 may dependon the resolution of the associated image sensors. For example, assumingsimilar line scan rates for both devices, if one device includes animage sensor having a resolution of 640×480 and another device includesan image sensor with a resolution of 1280×960, then more time will berequired to acquire a frame of image data from the sensor having thehigher resolution.

Another factor that may affect the timing of image data acquisition inimage capture devices 122, 124, and 126 is the maximum line scan rate.For example, acquisition of a row of image data from an image sensorincluded in image capture device 122, 124, and 126 will require someminimum amount of time. Assuming no pixel delay periods are added, thisminimum amount of time for acquisition of a row of image data will berelated to the maximum line scan rate for a particular device. Devicesthat offer higher maximum line scan rates have the potential to providehigher frame rates than devices with lower maximum line scan rates. Insome embodiments, one or more of image capture devices 124 and 126 mayhave a maximum line scan rate that is higher than a maximum line scanrate associated with image capture device 122. In some embodiments, themaximum line scan rate of image capture device 124 and/or 126 may be1.25, 1.5, 1.75, or 2 times or more than a maximum line scan rate ofimage capture device 122.

In another embodiment, image capture devices 122, 124, and 126 may havethe same maximum line scan rate, but image capture device 122 may beoperated at a scan rate less than or equal to its maximum scan rate. Thesystem may be configured such that one or more of image capture devices124 and 126 operate at a line scan rate that is equal to the line scanrate of image capture device 122. In other instances, the system may beconfigured such that the line scan rate of image capture device 124and/or image capture device 126 may be 1.25, 1.5, 1.75, or 2 times ormore than the line scan rate of image capture device 122.

In some embodiments, image capture devices 122, 124, and 126 may beasymmetric. That is, they may include cameras having different fields ofview (FOV) and focal lengths. The fields of view of image capturedevices 122, 124, and 126 may include any desired area relative to anenvironment of vehicle 200, for example. In some embodiments, one ormore of image capture devices 122, 124, and 126 may be configured toacquire image data from an environment in front of vehicle 200, behindvehicle 200, to the sides of vehicle 200, or combinations thereof.

Further, the focal length associated with each image capture device 122,124, and/or 126 may be selectable (e.g., by inclusion of appropriatelenses etc.) such that each device acquires images of objects at adesired distance range relative to vehicle 200. For example, in someembodiments image capture devices 122, 124, and 126 may acquire imagesof close-up objects within a few meters from the vehicle. Image capturedevices 122, 124, and 126 may also be configured to acquire images ofobjects at ranges more distant from the vehicle (e.g., 25 m, 50 m, 100m, 150 m, or more). Further, the focal lengths of image capture devices122, 124, and 126 may be selected such that one image capture device(e.g., image capture device 122) can acquire images of objectsrelatively close to the vehicle (e.g., within 10 m or within 20 m) whilethe other image capture devices (e.g., image capture devices 124 and126) can acquire images of more distant objects (e.g., greater than 20m, 50 m, 100 m, 150 m, etc.) from vehicle 200.

According to some embodiments, the FOV of one or more image capturedevices 122, 124, and 126 may have a wide angle. For example, it may beadvantageous to have a FOV of 140 degrees, especially for image capturedevices 122, 124, and 126 that may be used to capture images of the areain the vicinity of vehicle 200. For example, image capture device 122may be used to capture images of the area to the right or left ofvehicle 200 and, in such embodiments, it may be desirable for imagecapture device 122 to have a wide FOV (e.g., at least 140 degrees).

The field of view associated with each of image capture devices 122,124, and 126 may depend on the respective focal lengths. For example, asthe focal length increases, the corresponding field of view decreases.

Image capture devices 122, 124, and 126 may be configured to have anysuitable fields of view. In one particular example, image capture device122 may have a horizontal FOV of 46 degrees, image capture device 124may have a horizontal FOV of 23 degrees, and image capture device 126may have a horizontal FOV in between 23 and 46 degrees. In anotherinstance, image capture device 122 may have a horizontal FOV of 52degrees, image capture device 124 may have a horizontal FOV of 26degrees, and image capture device 126 may have a horizontal FOV inbetween 26 and 52 degrees. In some embodiments, a ratio of the FOV ofimage capture device 122 to the FOVs of image capture device 124 and/orimage capture device 126 may vary from 1.5 to 2.0. In other embodiments,this ratio may vary between 1.25 and 2.25.

System 100 may be configured so that a field of view of image capturedevice 122 overlaps, at least partially or fully, with a field of viewof image capture device 124 and/or image capture device 126. In someembodiments, system 100 may be configured such that the fields of viewof image capture devices 124 and 126, for example, fall within (e.g.,are narrower than) and share a common center with the field of view ofimage capture device 122. In other embodiments, the image capturedevices 122, 124, and 126 may capture adjacent FOVs or may have partialoverlap in their FOVs. In some embodiments, the fields of view of imagecapture devices 122, 124, and 126 may be aligned such that a center ofthe narrower FOV image capture devices 124 and/or 126 may be located ina lower half of the field of view of the wider FOV device 122.

FIG. 2F is a diagrammatic representation of exemplary vehicle controlsystems, consistent with the disclosed embodiments. As indicated in FIG.2F, vehicle 200 may include throttling system 220, braking system 230,and steering system 240. System 100 may provide inputs (e.g., controlsignals) to one or more of throttling system 220, braking system 230,and steering system 240 over one or more data links (e.g., any wiredand/or wireless link or links for transmitting data). For example, basedon analysis of images acquired by image capture devices 122, 124, and/or126, system 100 may provide control signals to one or more of throttlingsystem 220, braking system 230, and steering system 240 to navigatevehicle 200 (e.g., by causing an acceleration, a turn, a lane shift,etc.). Further, system 100 may receive inputs from one or more ofthrottling system 220, braking system 230, and steering system 24indicating operating conditions of vehicle 200 (e.g., speed, whethervehicle 200 is braking and/or turning, etc). Further details areprovided in connection with FIGS. 4-7, below.

As shown in FIG. 3A, vehicle 200 may also include a user interface 170for interacting with a driver or a passenger of vehicle 200. Forexample, user interface 170 in a vehicle application may include a touchscreen 320, knobs 330, buttons 340, and a microphone 350. A driver orpassenger of vehicle 200 may also use handles (e.g., located on or nearthe steering column of vehicle 200 including, for example, turn signalhandles), buttons (e.g., located on the steering wheel of vehicle 200),and the like, to interact with system 100. In some embodiments,microphone 350 may be positioned adjacent to a rearview mirror 310.Similarly, in some embodiments, image capture device 122 may be locatednear rearview mirror 310. In some embodiments, user interface 170 mayalso include one or more speakers 360 (e.g., speakers of a vehicle audiosystem). For example, system 100 may provide various notifications(e.g., alerts) via speakers 360.

FIGS. 3B-3D are illustrations of an exemplary camera mount 370configured to be positioned behind a rearview mirror (e.g., rearviewmirror 310) and against a vehicle windshield, consistent with disclosedembodiments. As shown in FIG. 3B, camera mount 370 may include imagecapture devices 122, 124, and 126. Image capture devices 124 and 126 maybe positioned behind a glare shield 380, which may be flush against thevehicle windshield and include a composition of film and/oranti-reflective materials. For example, glare shield 380 may bepositioned such that it aligns against a vehicle windshield having amatching slope, :In some embodiments, each of image capture devices 122,124, and 126 may be positioned behind glare shield 380, as depicted, forexample, in FIG. 3D. In embodiments in which one or more of imagecapture devices 122, 124, and 126 comprise an infrared image capturedevice, such devices may be positioned in front of glare shield 380 (or,alternatively, glare shield 380 may not extend in from of the infraredimage capture device(s)) in order to prevent such materials fromobstructing the intake of infrared light. The disclosed embodiments arenot limited to any particular configuration of image capture devices122, 124, and 126, camera mount 370, and glare shield 380. FIG. 3C is anillustration of camera mount 370 shown in FIG. 3B from a frontperspective.

As will be appreciated by a person skilled in the art having the benefitof this disclosure, numerous variations and/or modifications may be madeto the foregoing disclosed embodiments. For example, not all componentsare essential for the operation of system 100. Further, any componentmay be located in any appropriate part of system 100 and the componentsmay be rearranged into a variety of configurations while providing thefunctionality of the disclosed embodiments, Therefore, the foregoingconfigurations are examples and, regardless of the configurationsdiscussed above, system 100 can provide a wide range of functionality toanalyze the surroundings of vehicle 200 and navigate vehicle 200 inresponse to the analysis.

As discussed below in further detail and consistent with variousdisclosed embodiments, system 100 may provide a variety of featuresrelated to autonomous driving and/or driver assist technology. Forexample, system 100 may analyze image data, infrared image data,position data (e.g., GPS location information), map data, speed data,and/or data from sensors included in vehicle 200. System 100 may collectthe data for analysis from, for example, image acquisition unit 120,position sensor 130, and other sensors. Further, system 100 may analyzethe collected data to determine whether or not vehicle 200 should take acertain action, and then automatically take the determined actionwithout human intervention. For example, when vehicle 200 navigateswithout human intervention, system 100 may automatically control thebraking, acceleration, and/or steering of vehicle 200 (e.g., by sendingcontrol signals to one or more of throttling system 220, braking system230, and steering system 240). Further, system 100 may analyze thecollected data and issue warnings and/or alerts to vehicle occupantsbased on the analysis of the collected data. Additional detailsregarding the various embodiments that are provided by system 100 areprovided below,

Forward-Facing Multi-Imaging System

As discussed above, system 100 may provide drive assist functionalitythat uses a multi-camera system. The multi-camera system may use one ormore cameras (and/or infrared cameras) facing in the forward directionof a vehicle. In other embodiments, the multi-camera system may includeone or more cameras (and/or infrared cameras) facing to the side of avehicle or to the rear of the vehicle. In one embodiment, for example,system 100 may use a two-camera imaging system, where a first camera anda second camera (e.g., image capture devices 122 and 124) may bepositioned at the front and/or the sides of a vehicle (e.g., vehicle200). Other camera configurations are consistent with the disclosedembodiments, and the configurations disclosed herein are examples. Forexample, system 100 may include a configuration of any number of cameras(e.g., one, two, three, four, five, six, seven, eight, etc.) and of anycombination of types of cameras (e.g., two visual cameras and aninfrared camera, a visual camera and two infrared cameras, two visualcameras and two infrared cameras, etc.). Furthermore, system 100 mayinclude “dusters” of cameras. For example, a cluster of cameras(including any appropriate number of cameras, e.g., one, four, eight,etc., and any appropriate types of cameras, e.g., visual, infrared,etc.) may be forward-facing relative to a vehicle, or may be facing anyother direction (e.g., reward-facing, side-facing, at an angle, etc.)Accordingly, system 100 may include multiple clusters of cameras, witheach cluster oriented in a particular direction to capture images from aparticular region of a vehicle's environment.

The first camera may have a field of view that is greater than, lessthan, or partially overlapping with, the field of view of the secondcamera. In addition, the first camera may be connected to a first imageprocessor to perform monocular image analysis of images provided by thefirst camera, and the second camera may be connected to a second imageprocessor to perform monocular image analysis of images provided by thesecond camera. In embodiments in which one or more of the first cameraand second camera comprise an infrared camera, the first image processorand/or second image processor may perform a heat map analysis of heatmaps provided by the infrared camera(s).

The outputs (e.g., processed information) of the first and second imageprocessors may be combined. In some embodiments, the second imageprocessor may receive images from both the first camera and secondcamera to perform stereo analysis or to perform analysis on alignedvisual and infrared images. In another embodiment, system 100 may use athree-camera imaging system where each of the cameras has a differentfield of view. Such a system may, therefore, make decisions based oninformation derived from objects located at varying distances bothforward and to the sides of the vehicle. References to monocular imageanalysis may refer to instances where image analysis is performed basedon images captured from a single point of view (e.g., from a singlecamera). Stereo image analysis may refer to instances where imageanalysis is performed based on two or more images captured with one ormore variations of an image capture parameter. For example, capturedimages suitable for performing stereo image analysis may include imagescaptured: from two or more different positions, from different fields ofview, using different focal lengths, along with parallax information,etc. Hybrid image analysis may refer to instances where one or morevisual images are aligned with one or more infrared images and imageanalysis is performed based on the aligned image(s).

For example, in one embodiment, system 100 may implement a three cameraconfiguration using image capture devices 122, 124, and 126. In such aconfiguration, image capture device 122 may provide a narrow field ofview (e.g., 34 degrees, or other values selected from a range of about20 to 45 degrees, etc.), image capture device 124 may provide a widefield of view (e.g., 150 degrees or other values selected from a rangeof about 100 to about 180 degrees), and image capture device 126 mayprovide an intermediate field of view (e.g., 46 degrees or other valuesselected from a range of about 35 to about 60 degrees). in someembodiments, image capture device 126 may act as a main or primarycamera. Image capture devices 122, 124, and 126 may be positioned behindrearview mirror 310 and positioned substantially side-by-side (e.g., 6cm apart). Further, in some embodiments, as discussed above, one or moreof image capture devices 122, 124, and 126 may be mounted behind glareshield 380 that is flush with the windshield of vehicle 200. Suchshielding may act to minimize the impact of any reflections from insidethe car on image capture devices 122, 124, and 126.

In another embodiment, as discussed above in connection with FIGS. 3Band 3C, the wide field of view camera image capture device 124 in theabove example) may be mounted lower than the narrow and main field ofview cameras (e.g., image devices 122 and 126 in the above example).This configuration may provide a free line of sight from the wide fieldof view camera. To reduce reflections, the cameras may be mounted closeto the windshield of vehicle 200, and may include polarizers on thecameras to damp reflected light.

A three camera system may provide certain performance characteristics.For example, some embodiments may include an ability to validate thedetection of objects by one camera based on detection results fromanother camera. In the three camera configuration discussed above,processing unit 110 may include, for example, three processing devices(e.g., three EyeQ series of processor chips, as discussed above witheach processing device dedicated to processing images captured by one ormore of image capture devices 122, 124, and 126.

In a three camera system, a first processing device may receive imagesfrom both the main camera and the narrow field of view camera, andperform vision processing of the narrow FOV camera to, for example,detect other vehicles, pedestrians, lane marks, traffic signs, trafficlights, and other road objects. Further, the first processing device maycalculate a disparity of pixels between the images from the main cameraand the narrow camera and create a 3D reconstruction of the environmentof vehicle 200. The first processing device may then combine the 3Dreconstruction with 3D map data or with 3D information calculated basedon information from another camera.

The second processing device may receive images from the main camera andperform vision processing to detect other vehicles, pedestrians, lanemarks, traffic signs, traffic lights, and other road objects.Additionally, the second processing device may calculate a cameradisplacement and, based on the displacement, calculate a disparity ofpixels between successive images and create a 3D reconstruction of thescene (e.g., a structure from motion). The second processing device maysend the structure from motion based 3D reconstruction to the firstprocessing device to be combined with the stereo 3D images.

The third processing device may receive images from the wide FOV cameraand process the images to detect vehicles, pedestrians, lane marks,traffic signs, traffic lights, and other road objects. The thirdprocessing device may further execute additional processing instructionsto analyze images to identify objects moving in the image, such asvehicles changing lanes, pedestrians, etc.

In some embodiments, having streams of image-based information capturedand processed independently may provide an opportunity for providingredundancy in the system. Such redundancy may include, for example,using a first image capture device and the images processed from thatdevice to validate and/or supplement information obtained by capturingand processing image information from at least a second image capturedevice.

In some embodiments, system 100 may use two image capture devices (e.g.,image capture devices 122 and 124) in providing navigation assistancefor vehicle 200 and use a third image capture device (e.g., imagecapture device 126) to provide redundancy and validate the analysis ofdata received from the other two image capture devices. For example, insuch a configuration, image capture devices 122 and 124 may provideimages for stereo analysis by system 100 for navigating vehicle 200,while image capture device 126 may provide images for monocular analysisby system 100 to provide redundancy and validation of informationobtained based on images captured from image capture device 122 and/orimage capture device 124, That is, image capture device 126 (and acorresponding processing device) may be considered to provide aredundant sub-system for providing a check on the analysis derived fromimage capture devices 122 and 124 (e.g., to provide an automaticemergency braking (AEB) system). Furthermore, in some embodiments,redundancy and validation of received data may be supplemented based oninformation received from one more sensors (e.g., radar, lidar, acousticsensors, information received from one or more transceivers outside of avehicle, etc.).

One of skill in the art will recognize that the above cameraconfigurations, camera placements, number of cameras, camera locations,etc., are examples only. These components and others described relativeto the overall system may be assembled and used in a. variety ofdifferent configurations without departing from the scope of thedisclosed embodiments. Further details regarding usage of a multi-camerasystem to provide driver assist and/or autonomous vehicle functionalityfollow below.

FIG. 4 is an exemplary functional block diagram of memory 140 and/or150, which may be stored/programmed with instructions for performing oneor more operations consistent with the disclosed embodiments. Althoughthe following refers to memory 140, one of skill in the art willrecognize that instructions may be stored in memory 140 and/or 150.

As shown in FIG. 4, memory 140 may store a monocular image analysismodule 402, a stereo image analysis module 404, a velocity andacceleration module 406, and a navigational response module 408. Thedisclosed embodiments are not limited to any particular configuration ofmemory 140. Further, applications processor 180 and/or image processor190 may execute the instructions stored in any of modules 402, 404, 406,and 408 included in memory 140. One of skill in the art will understandthat references in the following discussions to processing unit 110 mayrefer to applications processor 180 and image processor 190 individuallyor collectively. Accordingly, steps of any of the following processesmay be performed by one or more processing devices.

In one embodiment, monocular image analysis module 402 may storeinstructions (such as computer vision software) which, when executed byprocessing unit 110, performs monocular image analysis of a set ofimages acquired by one of image capture devices 122, 124, and 126. Insome embodiments, processing unit 110 may combine information from a setof images with additional sensory information (e.g., information fromradar, lidar, etc.) to perform the monocular image analysis. Asdescribed in connection with FIGS. 5A-5D below, monocular image analysismodule 402 may include instructions for detecting a set of featureswithin the set of images, such as lane markings, vehicles, pedestrians,road signs, highway exit ramps, traffic lights, hazardous objects, andany other feature associated with an environment of a vehicle. Based onthe analysis, system 100 (e.g., via processing unit 110) may cause oneor more navigational responses in vehicle 200, such as a turn, a laneshift, a change in acceleration, and the like, as discussed below inconnection with navigational response module 408.

In one embodiment, stereo image analysis module 404 may storeinstructions (such as computer vision software) which, when executed byprocessing unit 110, performs stereo image analysis of first and secondsets of images acquired by a combination of image capture devicesselected from any of image capture devices 122, 124, and 126. In someembodiments, processing unit 110 may combine information from the firstand second sets of images with additional sensory information (e.g.,information from radar) to perform the stereo image analysis. Forexample, stereo image analysis module 404 may include instructions forperforming stereo image analysis based on a first set of images acquiredby image capture device 124 and a second set of images acquired by imagecapture device 126. As described in connection with FIG. 6 below, stereoimage analysis module 404 may include instructions for detecting a setof features within the first and second sets of images, such as lanemarkings, vehicles, pedestrians, road signs, highway exit ramps, trafficlights, hazardous objects, and the like. Based on the analysis,processing unit 110 may cause one or more navigational responses invehicle 200, such as a turn, a lane shift, a change in acceleration, andthe like, as discussed below in connection with navigational responsemodule 408. Furthermore, in some embodiments, stereo image analysismodule 404 may implement techniques associated with a trained system(such as a neural network or a deep neural network) or an untrainedsystem, such as a system that may be configured to use computer visionalgorithms to detect and/or label objects in an environment from whichsensory information was captured and processed. In one embodiment,stereo image analysis module 404 and/or other image processing modulesmay be configured to use a combination of a trained and untrainedsystem.

In one embodiment, velocity and acceleration module 406 may storesoftware configured to analyze data received from one or more computingand electromechanical devices in vehicle 200 that are configured tocause a change in velocity and/or acceleration of vehicle 200. Forexample, processing unit 110 may execute instructions associated withvelocity and acceleration module 406 to calculate a target speed forvehicle 200 based on data derived from execution of monocular imageanalysis module 402 and/or stereo image analysis module 404. Such datamay include, for example, a target position, velocity, and/oracceleration, the position and/or speed of vehicle 200 relative to anearby vehicle, pedestrian, or road object, position information forvehicle 200 relative to lane markings of the road, and the like. Inaddition, processing unit 110 may calculate a target speed for vehicle200 based on sensory input (e.g., information from radar) and input fromother systems of vehicle 200, such as throttling system 220, brakingsystem 230, and/or steering system 240 of vehicle 200. Based on thecalculated target speed, processing unit 110 may transmit electronicsignals to throttling system 220, braking system 230, and/or steeringsystem 240 of vehicle 200 to trigger a change in velocity and/oracceleration by, for example, physically depressing the brake or easingup off the accelerator of vehicle 200.

In one embodiment, navigational response module 408 may store softwareexecutable by processing unit 110 to determine a desired navigationalresponse based on data derived from execution of monocular imageanalysis module 402 and/or stereo image analysis module 404. Such datamay include position and speed information associated with nearbyvehicles, pedestrians, and road objects, target position information forvehicle 200, and the like. Additionally, in some embodiments, thenavigational response may be based (partially or fully) on map data, apredetermined position of vehicle 200, and/or a relative velocity or arelative acceleration between vehicle 200 and one or more objectsdetected from execution of monocular image analysis module 402 and/orstereo image analysis module 404. Navigational response module 408 mayalso determine a desired navigational response based on sensory input(e.g., information from radar) and inputs from other systems of vehicle200, such as throttling system 220, braking system 230, and steeringsystem 240 of vehicle 200. Based on the desired navigational response,processing unit 110 may transmit electronic signals to throttling system220, braking system 230, and steering system 240 of vehicle 200 totrigger a desired navigational response by, for example, turning thesteering wheel of vehicle 200 to achieve a rotation of a predeterminedangle. In some embodiments, processing unit 110 may use the output ofnavigational response module 408 (e.g., the desired navigationalresponse) as an input to execution of velocity and acceleration module406 for calculating a change in speed of vehicle 200.

Furthermore, any of the modules (e.g., modules 402, 404, and 406)disclosed herein may implement techniques associated with a trainedsystem (such as a neural network or a deep neural network) or anuntrained system.

FIG. 5A is a flowchart showing an exemplary process 500A for causing oneor more navigational responses based on monocular image analysis,consistent with disclosed embodiments. At step 510, processing unit 110may receive a plurality of images via data interface 128 betweenprocessing unit 110 and image acquisition unit 120. For instance, acamera included in image acquisition unit 120 (such as image capturedevice 122 having field of view 202) may capture a plurality of imagesof an area forward of vehicle 200 (or to the sides or rear of a vehicle,for example) and transmit them over a data connection (e.g., digital,wired, USB, wireless, Bluetooth, etc.) to processing unit 110.Processing unit 110 may execute monocular image analysis module 402 toanalyze the plurality of images at step 520, as described in furtherdetail in connection with FIGS. 5B-5D below. By performing the analysis,processing unit 110 may detect a set of features within the set ofimages, such as lane markings, vehicles, pedestrians, road signs,highway exit ramps, traffic lights, and the like.

Processing unit 110 may also execute monocular image analysis module 402to detect various road hazards at step 520, such as, for example, partsof a truck tire, fallen road signs, loose cargo, small animals, and thelike. Road hazards may vary in structure, shape, size, and color, whichmay make detection of such hazards more challenging. In someembodiments, processing unit 110 may execute monocular image analysismodule 402 to perform multi-frame analysis on the plurality of images todetect road hazards. For example, processing unit 110 may estimatecamera motion between consecutive image frames and calculate thedisparities in pixels between the frames to construct a 3D-map of theroad. Processing unit 110 may then use the 3D-map to detect the roadsurface, as well as hazards existing above the road surface.

At step 530, processing unit 110 may execute navigational responsemodule 408 to cause one or more navigational responses in vehicle 200based on the analysis performed at step 520 and the techniques asdescribed above in connection with FIG. 4. Navigational responses mayinclude, for example, a turn, a lane shift, a change in acceleration,and the like. In some embodiments, processing unit 110 may use dataderived from execution of velocity and acceleration module 406 to causethe one or more navigational responses. Additionally, multiplenavigational responses may occur simultaneously, in sequence, or anycombination thereof. For instance, processing unit 110 may cause vehicle200 to shift one lane over and then accelerate by, for example,sequentially transmitting control signals to steering system 240 andthrottling system 220 of vehicle 200. Alternatively, processing unit 110may cause vehicle 200 to brake while at the same time shifting lanes by,for example, simultaneously transmitting control signals to brakingsystem 230 and steering system 240 of vehicle 200.

FIG. 5B is a flowchart showing an exemplary process 500B for detectingone or more vehicles and/or pedestrians in a set of images, consistentwith disclosed embodiments. Processing unit 110 may execute monocularimage analysis module 402 to implement process 500B. At step 540,processing unit 110 may determine a set of candidate objectsrepresenting possible vehicles and/or pedestrians. For example,processing unit 110 may scan one or more images, compare the images toone or more predetermined patterns, and identify within each imagepossible locations that may contain objects of interest (e.g., vehicles,pedestrians, or portions thereof). The predetermined patterns may bedesigned in such a way to achieve a high rate of “false hits” and a lowrate of “misses.” For example, processing unit 110 may use a lowthreshold of similarity to predetermined patterns for identifyingcandidate objects as possible vehicles or pedestrians. Doing so mayallow processing unit 110 to reduce the probability of missing (e.g.,not identifying) a candidate object representing a vehicle orpedestrian.

At step 542, processing unit 110 may filter the set of candidate objectsto exclude certain candidates (e.g., irrelevant or less relevantobjects) based on classification criteria. Such criteria may be derivedfrom various properties associated with object types stored in adatabase (e.g., a database stored in memory 140). Properties may includeobject shape, dimensions, texture, position (e.g., relative to vehicle200), and the like. Thus, processing unit 110 may use one or more setsof criteria to reject false candidates from the set of candidateobjects,

At step 544, processing unit 110 may analyze multiple frames of imagesto determine whether objects in the set of candidate objects representvehicles and/or pedestrians. For example, processing unit 110 may tracka detected candidate object across consecutive frames and accumulateframe-by-frame data associated with the detected object (e.g., size,position relative to vehicle 200, etc.). Additionally, processing unit110 may estimate parameters for the detected object and compare theobject's frame-by-frame position data to a predicted position.

At step 546, processing unit 110 may construct a set of measurements forthe detected objects. Such measurements may include, for example,position, velocity, and acceleration values (e.g., relative to vehicle200) associated with the detected objects. In some embodiments,processing unit 110 may construct the measurements based on estimationtechniques using a series of time-based observations such as Kalmanfilters or linear quadratic estimation (LQE), and/or based on availablemodeling data for different object types (e.g., cars, trucks,pedestrians, bicycles, road signs, etc.). The Kalman filters may bebased on a measurement of an object's scale, where the scale measurementis proportional to a time to collision (e.g., the amount of time forvehicle 200 to reach the object). Thus, by performing steps 540, 542,544, and 546, processing unit 110 may identify vehicles and pedestriansappearing within the set of captured images and derive information(e.g., position, speed, size) associated with the vehicles andpedestrians. Based on the identification and the derived information,processing unit 110 may cause one or more navigational responses invehicle 200, as described in connection with FIG. 5A, above.

At step 548, processing unit 110 may perform an optical flow analysis ofone or more images to reduce the probabilities of detecting a “falsehit” and missing a candidate object that represents a vehicle orpedestrian. The optical flow analysis may refer to, for example,analyzing motion patterns relative to vehicle 200 in the one or moreimages associated with other vehicles and pedestrians, and that aredistinct from road surface motion. Processing unit 110 may calculate themotion of candidate objects by observing the different positions of theobjects across multiple image frames, which are captured at differenttimes. Processing unit 110 may use the position and time values asinputs into mathematical models for calculating the motion of thecandidate objects. Thus, optical flow analysis may provide anothermethod of detecting vehicles and pedestrians that are nearby vehicle200. Processing unit 110 may perform optical flow analysis incombination with steps 540, 542, 544, and 546 to provide redundancy fordetecting vehicles and pedestrians and increase the reliability ofsystem 100.

FIG. 5C is a flowchart showing an exemplary process 500C for detectingroad marks and/or lane geometry information in a set of images,consistent with disclosed embodiments. Processing unit 110 may executemonocular image analysis module 402 to implement process 500C. At step550, processing unit 110 may detect a set of objects by scanning one ormore images. To detect segments of lane markings, lane geometryinformation, and other pertinent road marks, processing unit 110 mayfilter the set of objects to exclude those determined to be irrelevant(e.g., minor potholes, small rocks, etc.). At step 552, processing unit110 may group together the segments detected in step 550 belonging tothe same road mark or lane mark. Based on the grouping, processing unit110 may develop a model to represent the detected segments, such as amathematical model.

At step 554, processing unit 110 may construct a set of measurementsassociated with the detected segments. In some embodiments, processingunit 110 may create a projection of the detected segments from the imageplane onto the real-world plane. The projection may be characterizedusing a 3rd-degree polynomial having coefficients corresponding tophysical properties such as the position, slope, curvature, andcurvature derivative of the detected road. In generating the projection,processing unit 110 may take into account changes in the road surface,as well as pitch and roll rates associated with vehicle 200. Inaddition, processing unit 110 may model the road elevation by analyzingposition and motion cues present on the road surface. Further,processing unit 110 may estimate the pitch and roll rates associatedwith vehicle 200 by tracking a set of feature points in the one or moreimages.

At step 556, processing unit 110 may perform multi-frame analysis by,for example, tracking the detected segments across consecutive imageframes and accumulating frame-by-frame data associated with detectedsegments. As processing unit 110 performs multi-frame analysis, the setof measurements constructed at step 554 may become more reliable andassociated with an increasingly higher confidence level. Thus, byperforming steps 550-556, processing unit 110 may identify road marksappealing within the set of captured images and derive lane geometryinformation. Based on the identification and the derived information,processing unit 110 may cause one or more navigational responses invehicle 200, as described in connection with FIG. 5A, above.

At step 558, processing unit 110 may consider additional sources ofinformation to further develop a safety model for vehicle 200 in thecontext of its surroundings. Processing unit 110 may use the safetymodel to define a context in which system 100 may execute autonomouscontrol of vehicle 200 in a safe manner. To develop the safety model, insome embodiments, processing unit 110 may consider the position andmotion of other vehicles, the detected road edges and barriers, and/orgeneral road shape descriptions extracted from map data (such as datafrom map database 160). By considering additional sources ofinformation, processing unit 110 may provide redundancy for detectingroad marks and lane geometry and increase the reliability of system 100.

FIG. 5D is a flowchart showing an exemplary process 500D for detectingtraffic lights in a set of images, consistent with disclosedembodiments. Processing unit 110 may execute monocular image analysismodule 402 to implement process 500D. At step 560, processing unit 110may scan the set of images and identify objects appearing at locationsin the images likely to contain traffic lights. For example, processingunit 110 may filter the identified objects to construct a set ofcandidate objects, excluding those objects unlikely to correspond totraffic lights. The filtering may be done based on various propertiesassociated with traffic lights, such as shape, dimensions, texture,position (e.g., relative to vehicle 200), and the like. Such propertiesmay be based on multiple examples of traffic lights and traffic controlsignals and stored in a database. In some embodiments, processing unit110 may perform multi-frame analysis on the set of candidate objectsreflecting possible traffic lights. For example, processing unit 110 maytrack the candidate objects across consecutive image frames, estimatethe real-world position of the candidate objects, and filter out thoseobjects that are moving (which are unlikely to be traffic lights). Insome embodiments, processing unit 110 may perform color analysis on thecandidate objects and identify the relative position of the detectedcolors appearing inside possible traffic lights.

At step 562, processing unit 110 may analyze the geometry of a junction.The analysis may be based on any combination of: (i) the number of lanesdetected on either side of vehicle 200, (ii) markings (such as arrowmarks) detected on the road, and (iii) descriptions of the junctionextracted from map data (such as data from map database 160). Processingunit 110 may conduct the analysis using information derived fromexecution of monocular analysis module 402. In addition, processing unit110 may determine a correspondence between the traffic lights detectedat step 560 and the lanes appearing near vehicle 200.

As vehicle 200 approaches the junction, at step 564, processing unit 110may update the confidence level associated with the analyzed junctiongeometry and the detected traffic lights. For instance, the number oftraffic lights estimated to appear at the junction as compared with thenumber actually appearing at the junction may impact the confidencelevel. Thus, based on the confidence level, processing unit 110 maydelegate control to the driver of vehicle 200 in order to improve safetyconditions. By performing steps 560, 562, and 564, processing unit 110may identify traffic lights appearing within the set of captured imagesand analyze junction geometry information. Based on the identificationand the analysis, processing unit 110 may cause one or more navigationalresponses in vehicle 200, as described in connection with FIG. 5A,above.

FIG. 5E is a flowchart showing an exemplary process 500E for causing oneor more navigational responses in vehicle 200 based on a vehicle path,consistent with the disclosed embodiments. At step 570, processing unit110 may construct an initial vehicle path associated with vehicle 200.The vehicle path may be represented using a set of points expressed incoordinates (x, z), and the distance d_(i) between two points in the setof points may fall in the range of 1 to 5 meters. In one embodiment,processing unit 110 may construct the initial vehicle path using twopolynomials, such as left and right road polynomials. Processing unit110 may calculate the geometric midpoint between the two polynomials andoffset each point included in the resultant vehicle path by apredetermined offset (e.g., a smart lane offset), if any (an offset ofzero may correspond to travel in the middle of a lane). The offset maybe in a direction perpendicular to a segment between any two points inthe vehicle path. in another embodiment, processing unit 110 may use onepolynomial and an estimated lane width to offset each point of thevehicle path by half the estimated lane width plus a predeterminedoffset (e.g., a smart lane offset).

At step 572, processing unit 110 may update the vehicle path constructedat step 570. Processing unit 110 may reconstruct the vehicle pathconstructed at step 570 using a higher resolution, such that thedistance d_(k) between two points in the set of points representing thevehicle path is less than the distance d_(i) described above. Forexample, the distance d_(k) may fall in the range of 0.1 to 0.3 meters.Processing unit 110 may reconstruct the vehicle path using a parabolicspline algorithm, which may yield a cumulative distance vector Scorresponding to the total length of the vehicle path (i.e., based onthe set of points representing the vehicle path).

At step 574, processing unit 110 may determine a look-ahead point(expressed in coordinates as (x_(l), z_(l))) based on the updatedvehicle path constructed at step 572. Processing unit 110 may extractthe look-ahead point from the cumulative distance vector S, and thelook-ahead point may be associated with a look-ahead distance andlook-ahead time. The look-ahead distance, which may have a lower boundranging from 10 to 20 meters, may be calculated as the product of thespeed of vehicle 200 and the look-ahead time. For example, as the speedof vehicle 200 decreases, the look-ahead distance may also decrease(e.g., until it reaches the lower bound). The look-ahead time, which mayrange from 0.5 to 1.5 seconds, may be inversely proportional to the gainof one or more control loops associated with causing a navigationalresponse in vehicle 200, such as the heading error tracking controlloop. For example, the gain of the heading error tracking control loopmay depend on the bandwidth of a yaw rate loop, a steering actuatorloop, car lateral dynamics, and the like. Thus, the higher the gain ofthe heading error tracking control loop, the lower the look-ahead time.

At step 576, processing unit 110 may determine a heading error and yawrate command based on the look-ahead point determined at step 574.Processing unit 110 may determine the heading error by calculating thearctangent of the look-ahead point, e.g., arctan (x_(l)/ z_(l)).Processing unit 110 may determine the yaw rate command as the product ofthe heading error and a high-level control gain. The high-level controlgain may be equal to: (2/look-ahead time), if the look-ahead distance isnot at the lower bound. Otherwise, the high-level control gain may beequal to: (2*speed of vehicle 200/look-ahead distance).

FIG. 5F is a flowchart showing an exemplary process 500F for determiningwhether a leading vehicle is changing lanes, consistent with thedisclosed embodiments. At step 580, processing unit 110 may determinenavigation information associated with a leading vehicle (e.g., avehicle traveling ahead of vehicle 200). For example, processing unit110 may determine the position, velocity (e.g., direction and speed),and/or acceleration of the leading vehicle, using the techniquesdescribed in connection with FIGS. 5A and 5B, above. Processing unit 110may also determine one or more road polynomials, a look-ahead point(associated with vehicle 200), and/or a snail trail (e.g., a set ofpoints describing a path taken by the leading vehicle), using thetechniques described in connection with FIG. 5E, above.

At step 582, processing unit 110 may analyze the navigation informationdetermined at step 580. In one embodiment, processing unit 110 maycalculate the distance between a snail trail and a road polynomial(e.g., along the trail). If the variance of this distance along thetrail exceeds a predetermined threshold (for example, 0.1 to 0.2 meterson a straight road, 0.3 to 0.4 meters on a moderately curvy road, and0.5 to 0.6 meters on a road with sharp curves), processing unit 110 maydetermine that the leading vehicle is likely changing lanes. In the casewhere multiple vehicles are detected traveling ahead of vehicle 200,processing unit 110 may compare the snail trails associated with eachvehicle. Based on the comparison, processing unit 110 may determine thata vehicle whose snail trail does not match with the snail trails of theother vehicles is likely changing lanes. Processing unit 110 mayadditionally compare the curvature of the snail trail (associated withthe leading vehicle) with the expected curvature of the road segment inwhich the leading vehicle is traveling. The expected curvature may beextracted from map data (e.g., data from map database 160), from roadpolynomials, from other vehicles' snail trails, from prior knowledgeabout the road, and the like. If the difference in curvature of thesnail trail and the expected curvature of the road segment exceeds apredetermined threshold, processing unit 110 may determine that theleading vehicle is likely changing lanes.

In another embodiment, processing unit 110 may compare the leadingvehicle's instantaneous position with the look-ahead point (associatedwith vehicle 200) over a specific period of time (e.g., 0.5 to 1.5seconds). If the distance between the leading vehicle's instantaneousposition and the look-ahead point varies during the specific period oftime, and the cumulative sum of variation exceeds a predeterminedthreshold (for example, 0.3 to 0.4 meters on a straight road, 0.7 to 0.8meters on a moderately curvy road, and 1.3 to 1.7 meters on a road withsharp curves), processing unit 110 may determine that the leadingvehicle is likely changing lanes. In another embodiment, processing unit110 may analyze the geometry of the snail trail by comparing the lateraldistance traveled along the trail with the expected curvature of thesnail trail. The expected radius of curvature may be determinedaccording to the calculation: (δ_(z) ²+δ_(x) ²)/2/(δ_(x)), where δ_(x)represents the lateral distance traveled and δ_(z) represents thelongitudinal distance traveled. If the difference between the lateraldistance traveled and the expected curvature exceeds a predeterminedthreshold (e.g., 500 to 700 meters), processing unit 110 may determinethat the leading vehicle is likely changing lanes. In anotherembodiment, processing unit 110 may analyze the position of the leadingvehicle. If the position of the leading vehicle obscures a roadpolynomial (e.g., the leading vehicle is overlaid on top of the roadpolynomial), then processing unit 110 may determine that the leadingvehicle is likely changing lanes. In the case where the position of theleading vehicle is such that, another vehicle is detected ahead of theleading vehicle and the snail trails of the two vehicles are notparallel, processing unit 110 may determine that the (closer) leadingvehicle is likely changing lanes.

At step 584, processing unit 110 may determine whether or not leadingvehicle 200 is changing lanes based on the analysis performed at step582. For example, processing unit 110 may make the determination basedon a weighted average of the individual analyses performed at step 582.Under such a scheme, for example, a decision by processing unit 110 thatthe leading vehicle is likely changing lanes based on a particular typeof analysis may be assigned a value of “1” (and “0” to represent adetermination that the leading vehicle is not likely changing lanes).Different analyses performed at step 582 may be assigned differentweights, and the disclosed embodiments are not limited to any particularcombination of analyses and weights. Furthermore, in some embodiments,the analysis may make use of trained system (e.g., a machine learning ordeep learning system), which may, for example, estimate a future pathahead of a current location of a vehicle based on an image captured atthe current location.

FIG. 6 is a flowchart showing an exemplary process 600 for causing oneor more navigational responses based on stereo image analysis,consistent with disclosed embodiments. At step 610, processing unit 110may receive a first and second plurality of images via data interface128. For example, cameras included in image acquisition unit 120 (suchas image capture devices 122 and 124 having fields of view 202 and 204)may capture a first and second plurality of images of an area forward ofvehicle 200 and transmit them over a digital connection (e.g., USB,wireless, Bluetooth, etc.) to processing unit 110. In some embodiments,processing unit 110 may receive the first and second plurality of imagesvia two or more data interfaces. The disclosed embodiments are notlimited to any particular data interface configurations or protocols.

At step 620, processing unit 110 may execute stereo image analysismodule 404. to perform stereo image analysis of the first and secondplurality of images to create a 3D map of the road in front of thevehicle and detect features within the images, such as lane markings,vehicles, pedestrians, road signs, highway exit ramps, traffic lights,road hazards, and the like. Stereo image analysis may be performed in amanner similar to the steps described in connection with FIGS. 5A-5D,above. For example, processing unit 110 may execute stereo imageanalysis module 404 to detect candidate objects (e.g., vehicles,pedestrians, road marks, traffic lights, road hazards, etc.) within thefirst and second plurality of images, filter out a subset of thecandidate objects based on various criteria, and perform multi-frameanalysis, construct measurements, and determine a confidence level forthe remaining candidate objects. In performing the steps above,processing unit 110 may consider information from both the first andsecond plurality of images, rather than information from one set ofimages alone. For example, processing unit 110 may analyze thedifferences in pixel-level data (or other data subsets from among thetwo streams of captured images) for a candidate object appearing in boththe first and second plurality of images. As another example, processingunit 110 may estimate a position and/or velocity of a candidate object(e.g., relative to vehicle 200) by observing that the object appears inone of the plurality of images but not the other or relative to otherdifferences that may exist relative to objects appealing in the twoimage streams. For example, position, velocity, and/or accelerationrelative to vehicle 200 may be determined based on trajectories,positions, movement characteristics, etc. of features associated with anobject appealing in one or both of the image streams.

At step 630, processing unit 110 may execute navigational responsemodule 408 to cause one or more navigational responses in vehicle 200based on the analysis performed at step 620 and the techniques asdescribed above in connection with FIG. 4. Navigational responses mayinclude, for example, a turn, a lane shift, a change in acceleration, achange in velocity, braking, and the like. In some embodiments,processing unit 110 may use data derived from execution of velocity andacceleration module 406 to cause the one or more navigational responses.Additionally, multiple navigational responses may occur simultaneously,in sequence, or any combination thereof.

FIG. 7 is a flowchart showing an exemplary process 700 for causing oneor more navigational responses based on an analysis of three sets ofimages, consistent with disclosed embodiments. At step 710, processingunit 110 may receive a first, second, and third plurality of images viadata interface 128. For instance, cameras included in image acquisitionunit 120 (such as image capture devices 122, 124, and 126 having fieldsof view 202, 204, and 206) may capture a first, second, and thirdplurality of images of an area forward and/or to the side of vehicle 200and transmit them over a digital connection (e.g., USB, wireless,Bluetooth, etc.) to processing unit 110. In some embodiments, processingunit 110 may receive the first, second, and third plurality of imagesvia three or more data interfaces. For example, each of image capturedevices 122, 124, 126 may have an associated data interface forcommunicating data to processing unit 110. The disclosed embodiments arenot limited to any particular data interface configurations orprotocols.

At step 720, processing unit 110 may analyze the first, second, andthird plurality of images to detect features within the images, such aslane markings, vehicles, pedestrians, road signs, highway exit ramps,traffic lights, road hazards, and the like. The analysis may beperformed in a manner similar to the steps described in connection withFIGS. 5A-5D and 6, above. For instance, processing unit 110 may performmonocular image analysis (e.g., via execution of monocular imageanalysis module 402 and based on the steps described in connection withFIGS. 5A-5D, above) on each of the first, second, and third plurality ofimages. Alternatively, processing unit 110 may perform stereo imageanalysis (e.g., via execution of stereo image analysis module 404 andbased on the steps described in connection with FIG. 6, above) on thefirst and second plurality of images, the second and third plurality ofimages, and/or the first and third plurality of images. The processedinformation corresponding to the analysis of the first, second, and/orthird plurality of images may be combined. In some embodiments,processing unit 110 may perform a combination of monocular and stereoimage analyses. For example, processing unit 110 may perform monocularimage analysis (e.g., via execution of monocular image analysis module402) on the first plurality of images and stereo image analysis (e.g.,via execution of stereo image analysis module 404) on the second andthird plurality of images. The configuration of image capture devices122, 124, and 126 including their respective locations and fields ofview 202, 204, and 206 may influence the types of analyses conducted onthe first, second, and third plurality of images. The disclosedembodiments are not limited to a particular configuration of imagecapture devices 122, 124, and 126, or the types of analyses conducted onthe first, second, and third plurality of images.

In some embodiments, processing unit 110 may perform testing on system100 based on the images acquired and analyzed at steps 710 and 720. Suchtesting may provide an indicator of the overall performance of system100 for certain configurations of image capture devices 122, 124, and126. For example, processing unit 110 may determine the proportion of“false hits” (e.g., cases where system 100 incorrectly determined thepresence of a vehicle or pedestrian) and “misses.”

At step 730, processing unit 110 may cause one or more navigationalresponses in vehicle 200 based on information derived from two of thefirst, second, and third plurality of images. Selection of two of thefirst, second, and third plurality of images may depend on variousfactors, such as, for example, the number, types, and sizes of objectsdetected in each of the plurality of images. Processing unit 110 mayalso make the selection based on image quality and resolution, theeffective field of view reflected in the images, the number of capturedframes, the extent to which one or more objects of interest actuallyappear in the frames (e.g., the percentage of frames in which an objectappears, the proportion of the object that appears in each such frame,etc.), and the like.

In some embodiments, processing unit 110 may select information derivedfrom two of the first, second, and third plurality of images bydetermining the extent to which information derived from one imagesource is consistent with information derived from other image sources.For example, processing unit 110 may combine the processed informationderived from each of image capture devices 122, 124, and 126 (whether bymonocular analysis, stereo analysis, or any combination of the two) anddetermine visual indicators (e.g., lane markings, a detected vehicle andits location and/or path, a detected traffic light, etc.) that areconsistent across the images captured from each of image capture devices122, 124, and 126. Processing unit 110 may also exclude information thatis inconsistent across the captured images (e.g., a vehicle changinglanes, a lane model indicating a vehicle that is too close to vehicle200, etc.). Thus, processing unit 110 may select information derivedfrom two of the first, second, and third plurality of images based onthe determinations of consistent and inconsistent information.

Navigational responses may include, for example, a turn, a lane shift, achange in acceleration, and the like. Processing unit 110 may cause theone or more navigational responses based on the analysis performed atstep 720 and the techniques as described above in connection with FIG.4. Processing unit 110 may also use data derived from execution ofvelocity and acceleration module 406 to cause the one or morenavigational responses. In some embodiments, processing unit 110 maycause the one or more navigational responses based on a relativeposition, relative velocity, and/or relative acceleration betweenvehicle 200 and an object detected within any of the first, second, andthird plurality of images. Multiple navigational responses may occursimultaneously, in sequence, or any combination thereof.

Analysis of captured images and/or heat maps may allow for detection ofparticular characteristics of both parked and moving vehicles.Navigational changes may be calculated based on the detectedcharacteristics. Embodiments for detection of particular characteristicsbased on one or more particular analyses of captured images and/or heatmaps will be discussed below with reference to FIGS. 8-28.

Detecting Car Door Opening Events

For example, identification of vehicles followed by identification ofwheel components of the identified vehicles may allow for targetedmonitoring for door opening events. By targeting the monitoring, thesystem may identify and react to door opening events with a shorterreaction time than traditional motion detection. Embodiments of thepresent disclosure described below relate to systems and methods fordetecting door opening events using targeted monitoring.

FIG. 8 is an exemplary functional block diagram of memory 140 and/or150, which may be stored/programmed with instructions for performing oneor more operations consistent with the disclosed embodiments. Althoughthe following refers to memory 140, one of skill in the art willrecognize that instructions may be stored in memory 140 and/or 150.

As shown in FIG. 8, memory 140 may store a vehicle side identificationmodule 802, a wheel identification module 804, a door edgeidentification module 806, and a navigational response module 808. Thedisclosed embodiments are not limited to any particular configuration ofmemory 140. Further, applications processor 180 and/or image processor190 may execute the instructions stored in any of modules 802-808included in memory 140. One of skill in the art will understand thatreferences in the following discussions to processing unit 110 may referto applications processor 180 and image processor 190 individually orcollectively. Accordingly, steps of any of the following processes maybe performed by one or more processing devices.

In one embodiment, vehicle side identification module 802 may storeinstructions (such as computer vision software) which, when executed byprocessing unit 110, performs analysis of one or more images acquired byone of image capture devices 122, 124, and 126. As described inconnection with FIGS. 9-14 below, vehicle side identification module 802may include instructions for determining bounding boxes marking thesides of one or more vehicles.

In one embodiment, wheel identification module 804 may storeinstructions (such as computer vision software) which, when executed byprocessing unit 110, performs analysis of one or more images acquired byone of image capture devices 122, 124, and 126. As described inconnection with FIGS. 9-14 below, wheel identification module 804 mayinclude instructions for determining ellipses marking the wheels of oneor more vehicles.

In one embodiment, door edge identification module 806 may storeinstructions (such as computer vision software) which, when executed byprocessing unit 110, performs analysis of one or more images acquired byone of image capture devices 122, 124, and 126. As described inconnection with FIGS. 9-14 below, door edge identification module 806may include instructions for identifying the appearance of a door edgeand monitoring the movement of an identified door edge.

In one embodiment, navigational response module 808 may store softwareexecutable by processing unit 110 to determine a desired navigationalresponse based on data derived from execution of vehicle sideidentification module 802, wheel identification module 804, and/or dooredge identification module 806. For example, navigational responsemodule 808 may cause a navigational change in accordance with method1400 of FIG. 14, described below.

Furthermore, any of the modules (e.g., modules 802, 804, and 806)disclosed herein may implement techniques associated with a trainedsystem (such as a neural network or a deep neural network) or anuntrained system.

FIG. 9 is a schematic view of a road 902 from a point-of-view of asystem included in a host vehicle consistent with the disclosedembodiments (e.g., system 100 described above). As depicted in FIG. 9,road 902 may have one or more parked vehicles (e.g., parked vehicle 904or parked vehicle 906).

System 100 may, for example, detect the parked vehicles using anattention mechanism which returns suspect patches and feeds the suspectpatches to a cascade of more and more complex classifiers to determineif the patch is in fact a vehicle. The attention mechanism andclassifiers may be trained on true and false patches, as describedbelow.

For example, system 100 may use an attention mechanism to detect therears of the parked vehicles (e.g., vehicle rears 908 and 910). Usingthe detected rears, system 100 may then detect the sides of the parkedvehicles (e.g., vehicle sides 912 and 914). Once detected, vehicle rearsand/or sides may be tracked.

To detect the bounding boxes depicted in FIG. 9, system 100 may input animage (e.g., an image from one of image capture devices 122, 124, or126) to one or more learned algorithms. The input image may, forexample, be an original 1280×9560 grayscale image. The learned algorithmmay output a scaled (e.g., 256×192) attention image. From the attentionimage, suspect patches may be identified and the (x, y) points of thesuspect patches may be scaled (e.g., by 5) to map onto the originalimage coordinates. The learned algorithm may use an (x, y) coordinate todefine the center of a suspect patch. The suspect patch may be scannedin the region of the (x, y) coordinate, for example, +/−5 pixels in eachdirection. In this example, then, each suspect patch produces 11×11total candidate patches.

Furthermore, in this example, each candidate patch is a square of size 2R+1, where R is the radius of a bounding ellipse. For example, asdepicted in FIG. 10, the vehicles (e.g., vehicle 1002) may haveassociated bounding ellipses (e.g., ellipse 1004) with centers (e.g.,center 1006).

The candidate patches may be scaled to a canonical size (such as 40×40)and are used as input to one or more trained networks, e.g., one or moreof the convolutional neural networks (CNNs) described below. Forexample, each of the one or more trained networks may score the inputpatches. Using the scores, each candidate patch may have a labelassigned based on the highest score. (In this example, the radius R andoriginal coordinates (x₀, y₀) may be used to map the candidate patchesback to the original image.)

Each candidate patch having a highest score above a threshold (which maybe preset or variable and may be learned from training) may then beinput to a final classifier. The final classifier may output (x, y)coordinates of three points on the bottom of the bounding box. Thesecoordinates may be scaled back to original image coordinates bymultiplying by an appropriate factor. In the example discussed above,the appropriate scaling factor may be (2 R+1)/40. In addition to using ascaling factor, the actual location (x₀, y₀) may be added to the scaledcoordinates (x, y). Using the unique label, the system may determinewhich two of the three (x, y) coordinates belong to the side (and whichside) and which two of the three (x, y) coordinates belong to the rearor front.

One skilled in the art would recognize that variations on this examplealgorithm are possible. For example, the size of the scaled attentionimage may vary, the shifting of the suspect patches may vary, the sizeof the candidate patches may vary, etc. By way of further example, theupper coordinates of the bounding box may also be computed. (In such anexample, the final classifier may output the (x, y) coordinates of threeadditional points.) In addition, other algorithms are possible, eitherin lieu of or in combination with the example algorithm discussed above.For example, other algorithms may include different and/or additionalclassifiers.

As explained above, the attention mechanism and subsequent classifiersmay be trained. For example, a training mechanism may employ over onemillion example images that, for example, may be 1280×960 grayscaleimages. In this example training set, the visible faces of the boundingbox may be marked as left, right, rear, or front. For example, they maybe shown as yellow, blue, red, and green, respectively. If a face ispartially visible, only the unobscured part may be marked in the imageand the partial obstruction noted in the database.

In this training example, for each bounding box, the system maycalculate the two most distant edges of the bounding box and construct abounded ellipse centered between the two edges and with a radius as thedistance to the farthest edges.

In this training example, the system may then extract the attentionimage from the whole, the attention image being a 256×192 image (thatis, reduced by a factor of 5). Each vehicle marked in the training imagemay be replaced by one point in the attention image located at thecoordinates of the center of the ellipse divided by 5, and the value ofthe point may be the radius of the bounding ellipse.

In this training example, the examples images may be used to train aconvolutional neural network (CNN). One skilled in the art wouldrecognize that other machine training techniques may be used either inlieu of or in conjunction with the CNN. The neural network may thus mapthe original image to the sparse, reduced resolution attention image.This approach may combine scene understanding (e.g., the location of theroad, the image perspective) with the local detection of something thatlooks like a car. Other design choices are possible. For example, thenetwork may first apply a filter bank designed to detect cars in placesthat cars are expected (e.g., not in the sky).

The neural network may send suspect patches to a first classifier thatmay score for each possible view. For example, the first classifier mayassign one of four main labels: LeftRear, LeftFront, RightRear,RightFront. If only one face is visible, then one of the two possiblelabels may be assigned randomly. Each main label may be furthersubdivided. For example, each main label may be subdivided into whetherthe patch contains more “side” than “end” or vise versa. Such asubdivision may be made, for example, by comparing the image width ofthe marked side and end faces. If the widths are equal, then thesubdivision may be assigned randomly.

By way of further example, each subdivision may be further divided intothree sub-subdivisions. For example, the “side” subdivision of theLeftRear label may contain three sub-subdivisions: “end” LeftRear10 maydesignate patches where the rear face is 10% or less the width of theleft face; “end” LeftRear50 may designate patches where the rear face ismore than 10% but less than 50%; and “end” LeftRear may designatepatches where the rear face is more than 50%.

By way of further example, each sub-subdivision may further be labeledif at least one face is obscured. In this example, the totalcombinations of sub-subdivisions and labels is 48. One skilled in theart would recognize that other means of divisions and labels that resultin the same or different total combinations are possible. For example,“side” LeftRear20 may designate patches where the left face is less than20% of the total; “side” LeftRear50 may designate patches where the leftface is 20% to 50% of the total; “side” LeftRear80 may designate patcheswhere the left face is 50% to 80% of the total; and “side” LeftRear100may designate patches where the left face is 80% to 100% of the total.

By way of further example, the four main labels may be replaced by twomain labels: ‘side/end’ and ‘end/side,’ depending on whether the sideface is viewed on the left or right of the end face, respectively. Thechoice of divisions and labels depends on the amount of available databecause, for larger numbers of subdivisions and labels, a larger numberof examples is required for training.

The neural network may thus input to the first classifier a patchcentered around a point in the attention map scaled back to the originalimage coordinates and exhaustively shifted +/−5 pixels in x and ydirections. Such a shifting generates 121 shifted examples. One skilledin the art would recognize that other means of generating shiftedexamples are possible. For example, the patch may be exhaustivelyshifted +/−4 (or +/−3 or the like) pixels in x and y directions.

In this example, each patch may he formed using the radius of thebounding ellipse R to cut a square of size 2 R+1 by 2 R+1 scaled to acanonical size (such as 40×40 pixels). One skilled in the art wouldrecognize that other means of generating patches are possible. Forexample, a square of size 2 R−1 by 2 R−1 may be cut. By way of furtherexample, one or more lengths of the bounding box may be used in place of2 R.

The neural network may input each labeled patch to a final classifier.The final classifier may output the location (x, y) in the labeled patchof each of three points defining the bottom of the bounding box. Theoutput (x, y) coordinates may be relative to the patch. In someembodiments, a neural network may be trained for each combination ofsubdivisions and labels. In other embodiments, fewer neural networks maybe trained.

Similar learning techniques may be used to train classifiers forextracting more specific features from identified vehicles. For example,classifiers may be trained to identify wheels, tires, the ‘A’ pillar, aside view mirror, or the like. In the example of FIG. 11, wheels 1102and 1104 of vehicle 1106 have been marked with ellipses. The system may,for example, draw the ellipses by scaling the bounding box to acanonical size (e.g., 40 pixels length and 20 pixels height) andinputting the scaled box to an appropriate classifier.

Based on identification of the wheels, the system may determine one ormore “hot spots” on the identified vehicle where door opening events maybe expected to occur. For example, one or more hot spots may be locatedbetween the identified tires and/or above the rear identified tire. Asdepicted in FIG. 12A, the one or more hot spots may be monitored for theappearance of a vertically oriented stripe 1202 on vehicle 1204. Theappearance of strip 1202 may indicate the beginning of a door openingevent.

In some embodiments, the system may use one or more features on vehicle1204 as fiducial points to track the motion of the edges of stripe 1202relative to a side of vehicle 1204. For example, the one or morefeatures may include the identified tires or other identified featuressuch as a front edge of vehicle 1204, one or more taillights of vehicle1204.

As depicted in FIG. 12B, stripe 1202 may expand as a door of vehicle1204 opens. Edge 1202 a of stripe 1202 may be fixed in position alongthe body of vehicle 1204 while edge 1202 b of stripe 1202 may appear tomove towards the front of vehicle 1204. The system may thus confirm thepresence of a door opening event based on monitoring of stripe 1202.

Based on the presence of a door opening event, the host vehicle mayundergo a navigational change. For example, as depicted in FIG. 13, theyaw 1301 of the host vehicle has changed, indicating that the hostvehicle is moving away from a door opening event.

FIG. 14 is a flowchart showing an exemplary process 1400 for causing oneor more navigational responses based on detection of a door openingevent, consistent with disclosed embodiments. At step 1402, processingunit 110 may receive at least one image of an environment of a hostvehicle via data interface 128. For instance, cameras included in imageacquisition unit 120 (such as image capture devices 122, 124, and 126having fields of view 202, 204, and 206) may capture at least one imageof an area forward and/or to the side of the host vehicle and transmitthem over a digital connection (e.g., USB, wireless, Bluetooth, etc.) toprocessing unit 110.

At step 1404, processing unit 110 may analyze the at least one image toidentify a side of a parked vehicle. Step 1404 may further includeassociating at least one bounding box with a shape of the side of theparked vehicle. For example, the analysis may be performed using alearned algorithm as discussed above with reference to FIGS. 9 and 10.

At step 1406, processing unit 110 may identify a structural feature ofthe parked vehicle. In some embodiments, processing unit 110 mayidentify a first structural feature of the parked vehicle in a forwardregion of the side of the parked vehicle and a second structural featureof the parked vehicle in a rear region of the side of the parkedvehicle. For example, a structural feature may include a wheel component(such as a tire, hubcap, or wheel structure), a mirror, an ‘A’ pillar, a‘B’ pillar, a ‘C’ pillar, or the like. The first and/or secondstructural features may be identified in a region in the vicinity of theidentified side. For example, the analysis may be performed using alearned algorithm as discussed above with reference to FIG. 11.

At step 1108, processing unit 110 may identify a door edge of the parkedvehicle. The door edge may be identified in a region in the vicinity ofthe structural feature(s). For example, in embodiments in whichprocessing unit 110 identifies a front wheel component and a rear wheelcomponent, the vicinity of the first and second wheels may include aregion between the front wheel component and the rear wheel component.By way of further example, in embodiments in which processing unit 110identifies a front wheel component and a rear wheel component, thevicinity of the first and second wheel components may include a regionabove the rear wheel component. The analysis may be performed using alearned algorithm as discussed above with reference to FIG. 12A.

At step 1410, processing unit 110 may determine a change of an imagecharacteristic of the door edge. For example, processing unit 110 maymonitor at least two images received from the image capture device foran appearance of a vertical stripe in the vicinity of the first andsecond wheel components as discussed above with reference to FIG. 12B.In this example, a first edge of the vertical stripe is fixed along abody of the parked vehicle in the monitored images, and a second edge ofthe vertical stripe moves toward a front region of the parked vehicle inthe monitored images. After appearing, the width of the door edge (thatis, the width of the vertical stripe) may then be tracked over time. Insome embodiments, the change of an image characteristic of the door edgemay include a widening of the door edge. In such embodiments,determining the change of an image characteristic of the door edge mayinclude monitoring an expansion of the vertical stripe.

In some embodiments, processing unit 110 may estimate the amount of dooropening. For example, processing unit 110 may extend the column of thefixed edge to intersect the bounding box and use they coordinate of theintersection to estimate the distance to the door. In this example, thedistance to the door and the width of the stripe may be used to estimatethe amount of door opening. Accordingly, processing unit 110 maydetermine a distance that the door edge extends away from the parkedvehicle based on the determined width. Accordingly, the separation ofthe door edge from the body of the vehicle (that is, the amount of dooropening) may be tracked over time.

At step 1412, processing unit 110 may alter a navigational path of thehost vehicle. For example, navigational responses may include a turn (asdepicted in FIG. 13), a lane shift, a change in acceleration, and thelike. Processing unit 110 may cause the one or more navigationalresponses based on the determination performed at step 1410. Forexample, processing unit 110 may move the host vehicle away from thedoor edge event and/or decelerate the host vehicle in response to thedoor edge event. In this example, processing unit 110 may determine alateral safety distance for the host vehicle based on the determineddistance that the door edge extends away from the parked vehicle, andthe alteration of the navigational path of the host vehicle may bebased, at least in part, on the determined lateral safety distance. Inanother example, processing unit 110 may determine a lateral safetydistance for the host vehicle based on a predefined value, such as avalue which corresponds to a typical extent of a protrusion that isassociated with a vehicle's door opening. Further, by way of example, adifferent predefined value may be used for different types of vehiclesand, for example, for other sizes of vehicles, such as a truck, a largerpredefined safety distance value may be used, compared to the safetydistance value that is used for a smaller vehicle.

Processing unit 110 may also use data derived from execution of velocityand acceleration module 406 to cause the one or more navigationalresponses. Multiple navigational responses may occur simultaneously, insequence, or any combination thereof. For example, the navigationalresponse may be determined by a trained system. Further, by way ofexample, the trained system may be configured to avoid compromisingcertain safety constraints while optimizing performance, and the trainedsystem may be configured to react to a door opening detection byinvoking a navigational change. In another example, a set of rules maybe used to determine a desired response when a door opening (of a parkedcar) event is detected.

Detecting a Vehicle Entering a Host Vehicle's Lane

Systems and methods that identify road homography and identify wheelcomponents of the vehicles may allow for targeted monitoring formovement of the vehicles. By targeting the monitoring, the system mayidentify and react to motion into a host vehicle's lane, either fromanother lane or from a parked position, with a shorter reaction time, atleast under certain circumstances, than traditional motion detection.Embodiments of the present disclosure described below relate to systemsand methods for detecting a vehicle entering a host vehicle's lane usingtargeted monitoring.

FIG. 15 is an exemplary functional block diagram of memory 140 and/or150, which may be stored/programmed with instructions for performing oneor more operations consistent with the disclosed embodiments. Althoughthe following refers to memory 140, one of skill in the art willrecognize that instructions may be stored in memory 140 and/or 150.

As shown in FIG. 15, memory 140 may store a road homography module 1502,a wheel identification module 1504, a motion analysis module 1506, and anavigational response module 1508. The disclosed embodiments are notlimited to any particular configuration of memory 140. Further,applications processor 180 and/or image processor 190 may execute theinstructions stored in any of modules 1502-1508 included in memory 140.One of skill in the art will understand that references in the followingdiscussions to processing unit 110 may refer to applications processor180 and image processor 190 individually or collectively. Accordingly,steps of any of the following processes may be performed by one or moreprocessing devices.

In one embodiment, road homography module 1502 may store instructions(such as computer vision software) which, when executed by processingunit 110, warps the homography of a road in one or more images acquiredby one of image capture devices 122, 124, and 126. For example, roadhomography module 1502 may include instructions for executing method1800 of FIG. 18, described below.

In one embodiment, wheel identification module 1504 may storeinstructions (such as computer vision software) which, when executed byprocessing unit 110, performs analysis of one or more images acquired byone of image capture devices 122, 124, and 126. As described inconnection with FIGS. 9-14 above, wheel identification module 1504 mayinclude instructions for determining ellipses marking the wheels of oneor more vehicles.

In one embodiment, motion analysis module 1506 may store instructions(such as computer vision software) which, when executed by processingunit 110, performs analysis of one or more images acquired by one ofimage capture devices 122, 124, and 126 (and/or of one or more imagesprocessed by road homography module 1502) to track the motion of one ormore identified vehicle components. For example, in combination withwheel identification module 1504, motion analysis module 1506 may trackthe motion of an identified wheel component of a vehicle over time.

In one embodiment, navigational response module 1508 may store softwareexecutable by processing unit 110 to determine a desired navigationalresponse based on data derived from execution of road homography module1502, wheel identification module 1504, and/or motion analysis module1506. For example, navigational response module 1508 may cause anavigational change in accordance with method 1700 of FIG. 17, describedbelow.

Furthermore, any of the modules (e.g., modules 1502, 1504, and 1506)disclosed herein may implement techniques associated with a trainedsystem (such as a neural network or a deep neural network) or anuntrained system.

FIG. 16A depicts a parked car 1602 from a point-of-view of a systemconsistent with the disclosed embodiments. In the example of FIG. 16A,wheels 1604 and 1606 of vehicle 1602 have been marked with ellipses. Thesystem may, for example, draw the ellipses by scaling the bounding boxto a canonical size (e.g., 40 pixels length and 20 pixels height) andinputting the scaled box to an appropriate classifier, as describedabove.

FIG. 16B depicts vehicle 1602 pulling away from a parked (that is,stationary) state. In the example of FIG. 16, wheels 1604 and 1606 ofvehicle 1602 are now rotating as vehicle 1602 moves. The system maytrack the rotation of wheels 1604 and 1606, as discussed below, todetermine that vehicle 1602 is moving.

FIG. 17 is a flowchart showing an exemplary process 1700 for causing oneor more navigational responses based on detection of a target vehicleentering the host vehicle's lane, consistent with disclosed embodiments.At step 1702, processing unit 110 may receive a plurality of images ofan environment of a host vehicle via data interface 128. For instance,cameras included in image acquisition unit 120 (such as image capturedevices 122, 124, and 126 having fields of view 202, 204, and 206) maycapture a plurality of images of an area forward and/or to the side ofthe host vehicle and transmit them over a digital connection (e.g., USB,wireless, Bluetooth, etc.) to processing unit 110.

The plurality of images may be collected over time. For example, theplurality may include a first image captured at time t=0, a second imagecaptured at t=0.5 seconds, and a third image capture at t=1.0 seconds.The timing between the images may depend at least upon the scan rates ofthe one or more image capture devices.

At step 1704, processing unit 110 may analyze the at least one image toidentify a target vehicle. For example, identifying a target vehicle mayfurther include associating at least one bounding box with a shape ofthe side of the target vehicle. For example, the analysis may beperformed using a learned algorithm as discussed above with reference toFIGS. 9 and 10. In some embodiments, step 1704 may further includeidentifying a wheel on the side of the identified target vehicle. Forexample, the analysis may be performed using a learned algorithm asdiscussed above with reference to FIG. 11.

Step 1704 is not limited to a wheel but may also include a wheelcomponent. example, processing unit 110 may identify a wheel componentthat includes at least one of a tire, a hubcap, or a wheel structure.

At step 1706, processing unit 110 may identify motion associated withthe identified wheel(s). For example, processing unit may identify themotion in a region including at least one wheel component of the targetvehicle, which may include a region adjacent to a road surface. Bymonitoring the at least two of the plurality of images, processing unitmay identify motion using an indication of rotation of the at least onewheel component.

By way of further example, processing unit 110 may identify at least onefeature associated with the at least one wheel component (e.g., a logoon the wheel, a measurement of a tire of the wheel, a measurement of ahubcap of the wheel, a particular patch of pixels). Using the at leastone feature, processing unit 110 may identify an indicator of apositional change of the at least one feature (e.g., blurting of thelogo, changed coordinates of the patch of pixels).

By way of further example, processing unit 110 may warp the homographyof the road, as described with respect to method 1800 of FIG. 18 below,identify the point(s) of contact between the identified wheels) andwarped road (which may be stationary), and track points above thepoint(s) of contact to identify the motion.

Processing unit 110 may use indicators of rotation, positional changesof the at least one feature, and/or tracked points to determine a speedat which the target vehicle is moving. The processing unit 110 may usethe determined speed in causing a navigation change in step 1708,described below. In addition, processing unit 110 may estimate adistance to the tire using a ground plane constraint and may estimate alateral motion of the target vehicle based on the estimated distance.

At step 1708, processing unit 110 may cause a navigational change of thehost vehicle. For example, navigational responses may include a changein a heading direction of the host vehicle (as depicted in FIG. 13above), a lane shift, a change in acceleration (e.g., applying brakes ofthe host vehicle), and the like. Processing unit 110 may cause the oneor more navigational responses based on the determination performed atstep 1706. For example, processing unit 110 may move the host vehicleaway from the target vehicle and/or decelerate the host vehicle inresponse to movement of the target vehicle. In this example, processingunit 110 may determine a lateral safety distance for the host vehiclebased on the determined speed of the target vehicle (and/or theestimated lateral motion), and the alteration of the navigational pathof the host vehicle may be based, at least in part, on the determinedlateral safety distance.

Processing unit 110 may also use data derived from execution of velocityand acceleration module 406 to cause the one or more navigationalresponses. Multiple navigational responses may occur simultaneously, insequence, or any combination thereof.

FIG. 18 is a flowchart showing an exemplary process 1800 for warping thehomography of a road. At step 1802, processing unit 110 may receive aplurality of images of an environment of a host vehicle via datainterface 128. For instance, cameras included in image acquisition unit120 (such as image capture devices 122, 124, and 126 having fields ofview 202, 204, and 206) may capture a plurality of images of an areaforward and/or to the side of the host vehicle and transmit them over adigital connection (e.g., USB, wireless, Bluetooth, etc.) to processingunit 110.

The plurality of images may be collected over time. For example, theplurality may include a first image captured at time t=0 and a secondimage captured at t=0.5 seconds. The timing between the images maydepend at least upon the scan rates of the one or more image capturedevices.

At step 1804, processing unit 110 may initially warp a first of theplurality of images toward a second of the plurality of images. Forexample, one of the first or second images may be rotated based onestimates of yaw, pitch, and roll of the host vehicle.

At step 1806, processing unit 110 may select a grid of points within thefirst or second image as a grid of reference points. For example, thegrid may be formed from any shape, for example, an ellipse, a rectangle,a trapezoid, etc. Alternatively, a random distribution of points may beselected.

At step 1808, processing unit 110 may locate patches around the selectedgrid. For example, the patches may be of uniform size and shape.Alternatively, the patches may vary and/or may vary randomly.

At step 1810, processing unit 110 may track the selected grid of pointsusing, for example, normalized correlation computations based on thepatches. From the tracked points, processing unit 110 may select asubset of points with the highest scores based on the tracking.

At step 1812, processing unit 110 may fit the tracked points to ahomography. In some embodiments, processing unit 110 may use randomsubsets of the tracked points to compute multiple homographies. In suchembodiments, processing unit 110 may retain the random subset with thehighest scoring homography.

At step 1814, processing unit 110 may revise the initial warping usingthe homography from step 1812. For example, processing unit 110 may usethe random subset of points with the highest scoring homography in orderto rewarp the first of the plurality of images toward the second of theplurality of images. Processing unit 110 may further calculate a leastsquares homography directly from the rewarped images. One skilled in theart would recognize that other algorithms for calculating roadhomography may be used.

Detecting a One-way Road Based on Parked Vehicle Direction

Systems and methods that identify vehicles and identify fronts and/orrears of the identified vehicles may allow for detection of one-wayroads. By using identified fronts and/or rears of vehicles, one-wayroads may be detected without having to interpret signs or even when novehicles are driving on the road. Embodiments of the present disclosuredescribed below relate to systems and methods for detecting a one-wayroad based on parked vehicle direction.

FIG. 19 is an exemplary functional block diagram of memory 140 and/or150, which may be stored/programmed with instructions for performing oneor more operations consistent with the disclosed embodiments. Althoughthe following refers to memory 140, one of skill in the art willrecognize that instructions may be stored in memory 140 and/or 150.

As shown in FIG. 19, memory 140 may store a vehicle identificationmodule 1902, a direction determination module 1904, a vehicle sideidentification module 1906, and a navigational response module 1908. Thedisclosed embodiments are not limited to any particular configuration ofmemory 140. Further, applications processor 180 and/or image processor190 may execute the instructions stored in any of modules 1902-1908included in memory 140. One of skill in the art will understand thatreferences in the following discussions to processing unit 110 may referto applications processor 180 and image processor 190 individually orcollectively. Accordingly, steps of any of the following processes maybe performed by one or more processing devices.

In one embodiment, vehicle identification module 1902 may storeinstructions (such as computer vision software) which, when executed byprocessing unit 110, performs analysis of one or more images acquired byone of image capture devices 122, 124, and 126. As described inconnection with FIGS. 9-14 above, vehicle identification module 1902 mayinclude instructions for determining bounding boxes of one or morevehicles.

In one embodiment, direction determination module 1904 may storeinstructions (such as computer vision software) which, when executed byprocessing unit 110, performs analysis of one or more images acquired byone of image capture devices 122, 124, and 126. As described inconnection with FIGS. 20A and 20B below, direction determination module1904 may include instructions for determining a facing direction for theidentified vehicles.

For example, a facing direction may indicate whether an identifiedvehicle that is parallel parked faces toward or away from a hostvehicle. By way of further example, a facing direction, or “tiltingdirection,” may indicate whether an identified vehicle that is parked inan angled spot is tilted toward or away from a host vehicle. in such anexample, the facing direction may further indicate whether theidentified vehicle has been backed into the angled spot or pulledforward into the spot.

In one embodiment, vehicle side identification module 1906 may storeinstructions (such as computer vision software) which, when executed byprocessing unit 110, performs analysis of one or more images acquired byone of image capture devices 122, 124, and 126. As described inconnection with FIGS. 9-14 above, vehicle side identification module1906 may include instructions for classifying the identified boundingboxes of one or more vehicles.

In one embodiment, navigational response module 1908 may store softwareexecutable by processing unit 110 to determine a desired navigationalresponse based on data derived from execution of vehicle identificationmodule 1902, direction determination module 1904, and/or vehicle sideidentification module 1906. For example, navigational response module1908 may cause a navigational change in accordance with method 2100 ofFIG. 21, described below.

Furthermore, any of the modules (e.g., modules 1902, 1904, and 1906)disclosed herein may implement techniques associated with a trainedsystem (such as a neural network or a deep neural network) or anuntrained system.

FIG. 20A depicts a one-way road 2002 from a point-of-view of a systemconsistent with the disclosed embodiments. Road 2002 may include a firstplurality of stationary vehicles on one side (e.g., first vehicle 2004)and may include a second plurality of stationary vehicles on anotherside (e.g., second vehicle 2006). As described below with respect tomethod 2100 of FIG. 21, the system may determine facing directions forthe vehicles in the first plurality and facing directions for thevehicles in the second plurality.

As depicted in FIG. 20A, the system may determine that road 2002 is aone-way road if the facing directions for both the first plurality andthe second plurality are the same. In other embodiments in which oneside of road 2002 has angled parking rather than parallel parking, thesystem may determine that road 2002 is a one-way road if the facingdirection for the parallel parking side is the same as the tiltingdirection for the angled parking side. In still other embodiments inwhich road 2002 has two sides of angled parking, the system maydetermine that road 2002 is a one-way road if the tilting directions forboth the first plurality and the second plurality are the same. Incertain aspects, the determination may depend on whether the firstand/or second plurality are backed into angled spots or pulled forwardinto angled spots.

FIG. 20B also depicts a one-way road 2008 from a point-of-view of asystem consistent with the disclosed embodiments. Similar to road 2002,road 2008 may include a first plurality of stationary vehicles on oneside (e.g., first vehicle 2010) and may include a second plurality ofstationary vehicles on another side (e.g., second vehicle 2012). Asdescribed below with respect to method 2100 of FIG. 21, the system maydetermine facing directions for the vehicles in the first plurality andfacing directions for the vehicles in the second plurality. As depictedin FIG. 20A, the system may determine that a vehicle (e.g., vehicle2010) is parked incorrectly if its facing direction differs from thefacing directions of its associated plurality of vehicles. For example,the system may determine that a vehicle is parked incorrectly if anumber of other vehicles having a different facing direction in theassociated plurality is beyond a threshold. By way of further example,the system may determine that a vehicle is parked in correctly if aratio of other vehicles having a different facing direction to vehicleshaving the same facing direction is beyond a threshold (e.g., fiftypercent or greater, sixty percent or greater, seventy percent orgreater, etc.). In some embodiments, this determination may be used toissue a traffic ticket to (or instruct the operator of the host vehicleto issue a traffic ticket to) the owner or operator of vehicle 2010.

FIG. 21 is a flowchart showing an exemplary process 2100 for causing oneor more navigational responses based on detection of whether a road onwhich the host vehicle travels is a one-way road, consistent withdisclosed embodiments. At step 2102, processing unit 110 may receive atleast one image of an environment of a host vehicle via data interface128. For instance, cameras included in image acquisition unit 120 (suchas image capture devices 122, 124, and 126 having fields of view 202,204, and 206) may capture at least one image of an area forward and/orto the side of the host vehicle and transmit them over a digitalconnection (e.g., USB, wireless, Bluetooth, etc.) to processing unit110.

At step 2104, processing unit 110 may analyze the at least one image toidentify a first plurality of vehicles on one side of a road. Forexample, identifying a first plurality of vehicles may further includeassociating bounding boxes with shapes of the first plurality ofvehicles. For example, the analysis may be performed using a learnedalgorithm as discussed above with reference to FIGS. 9 and 10.

Step 2104 may further include identifying, based on analysis of the atleast one image, a side of at least one of the first plurality ofvehicles or at least one of the second plurality of vehicles. Forexample, the analysis may be performed using a learned algorithm asdiscussed above with reference to FIGS. 9 and 10. In some embodiments,the identification of the side may be based on at least two featuresassociated with at least one of the first plurality of vehicles or atleast one of the second plurality of vehicles. For example, featuresassociated with a vehicle may include mirrors, windows, door handles,door shapes, a number of doors, an incline of the front and/or rearwindshield, or the like. In some embodiments, the identified side may bea right side. In other embodiments, the identified side may be a leftside.

At step 2106, processing unit 110 may analyze the at least one image toidentify a second plurality of vehicles on another side of a road. Step2106 may be performed analogously to and/or simultaneously with step2104.

At step 2108, processing unit 110 may determine a first facing directionfor the first plurality of vehicles. In some embodiments, the firstplurality of vehicles may all have the same facing direction. In otherembodiments, the facing directions may differ.

At step 2110, processing unit 110 may determine a second facingdirection for the second plurality of vehicles. In some embodiments, thesecond plurality of vehicles may all have the same facing direction. Inother embodiments, the facing directions may differ.

At step 2112, processing unit 110 may cause a navigational change of thehost vehicle. For example, navigational responses may include a turn (asdepicted in FIG. 13 above), a lane shift, a change in acceleration(e.g., applying brakes of the host vehicle), and the like. Processingunit 110 may cause the one or more navigational responses based on thedetermination performed at step 2112. For example, processing unit 110may determine that the road is a one-way road based on the first andsecond facing directions. Based on this determination, processing unitmay slow or stop the host vehicle and/or execute a U-turn.

In some embodiments, method 2100 may include additional steps. Forexample, method 2100 may include receiving a navigation instruction tonavigate the host vehicle from a first road on which the host vehicle istraveling to a second road. The navigation instruction may include aninstruction to turn the host vehicle onto the second road, to veer ontothe second road, to merge onto a ramp that proceeds onto the secondroad, or the like.

In such embodiments, method 2100 may further include determining thatthe first facing direction and the second facing direction are bothopposite to a heading direction the host vehicle would travel if thehost vehicle were to turn onto the second road. For example, processingunit 110 may analyze images of vehicles on the second road to determinethe first and second facing directions and then determine whether theyare opposite to the projected heading direction of the host vehicle. Inresponse to the determination the first facing direction and the secondfacing direction are both opposite to the heading direction the hostvehicle would travel if the host vehicle were to turn onto the secondroad, processing unit 110 may suspend the navigation instruction. Forexample, processing unit 110 may cancel the instruction to turn, veer,merge, or the like onto the second road because processing unit 110 hasdetermined that the road is a one-way road in a direction opposite tothe projected heading direction.

In further embodiments, method 2100 may include receiving an overrideinstruction to reinstate the suspended navigation instruction. Forexample, the override instruction may be initiated based on a manualconfirmation received from a person inside the host vehicle, initiatedbased on accessing map data, initiated based on crowdsourced datarelated to a travel direction of the second road, or the like.

Processing unit 110 may also use data derived from execution of velocityand acceleration module 406 to cause the one or more navigationalresponses. Multiple navigational responses may occur simultaneously, insequence, or any combination thereof.

Predicting a State of a Parked Vehicle Based on a Heat Profile

Systems and methods that predict states of parked vehicles based on heatprofiles may allow for prediction of a movement of the parked vehiclesbefore the vehicles begin to move. In this way, the system may identifypredicted movement and preemptively adjust thereto rather than waitinguntil motion is actually detected like traditional motion detection.Embodiments of the present disclosure described below relate to systemsand methods for predicting a state of a parked vehicle based on a heatprofile.

FIG. 22 is an exemplary functional block diagram of memory 140 and/or150, which may be stored/programmed with instructions for performing oneor more operations consistent with the disclosed embodiments. Althoughthe following refers to memory 140, one of skill in the art willrecognize that instructions may be stored in memory 140 and/or 150.

As shown in FIG. 22, memory 140 may store a visual-infrared alignmentmodule 2202, a vehicle identification module 2204, a state predictionmodule 2206, and a navigational response module 2208. The disclosedembodiments are not limited to any particular configuration of memory140. Further, applications processor 180 and/or image processor 190 mayexecute the instructions stored in any of modules 2202-2208 included inmemory 140. One of skill in the art will understand that references inthe following discussions to processing unit 110 may refer toapplications processor 180 and image processor 190 individually orcollectively. Accordingly, steps of any of the following processes maybe performed by one or more processing devices.

In one embodiment, visual-infrared alignment module 2202 may storeinstructions (such as computer vision software) which, when executed byprocessing unit 110, aligns one or more visual images acquired by one ofimage capture devices 122, 124, and 126 with one or more infrared images(that is, heat maps) acquired by one of image capture devices 122, 124,and 126. For example, visual-infrared module 2202 may includeinstructions for executing method 2500 of FIG. 25, described below.

In one embodiment, vehicle identification module 2204 may storeinstructions (such as computer vision software) which, when executed byprocessing unit 110, performs analysis of one or more images acquired byone of image capture devices 122, 124, and 126. As described inconnection with FIGS. 9-14 below, vehicle identification module 2202 mayinclude instructions for determining bounding boxes of one or morevehicles.

In one embodiment, state prediction module 2206 may store instructions(such as computer vision software) which, when executed by processingunit 110, performs analysis of one or more aligned images fromvisual-infrared alignment module 2202 to predict the state of one ormore identified vehicles. For example, state prediction module 2206 mayoutput a predicted states based on visual indicators and heat indicatorsof an identified vehicle.

In one embodiment, navigational response module 2208 may store softwareexecutable by processing unit 110 to determine a desired navigationalresponse based on data derived from execution of visual-infraredalignment module 2202, vehicle identification module 2204, and/or stateprediction module 2206. For example, navigational response module 2208may cause a navigational change in accordance with method 2400 of FIG.24, described below.

Furthermore, any of the modules (e.g., modules 2202, 2204, and 2206)disclosed herein may implement techniques associated with a trainedsystem (such as a neural network or a deep neural network) or anuntrained system.

FIG. 23A depicts a parked vehicle 2302 from a point-of-view of a systemconsistent with the disclosed embodiments. For example, vehicle 2302 maybe monitored by the system for a change in illumination state and/or fortemperature characteristics. The system may, for example, use method2400 of FIG. 24 to determine a predicted state of vehicle 2302 based ona change in illumination state and/or temperature characteristics.

FIG. 23B depicts parked vehicle 2302 having a changed illuminationstate. In the example of FIG. 23C, taillights 2304 a and 2304 b ofvehicle 2302 have change from a non-illuminated state to an illuminatedstate. Other embodiments in which headlights indicate a changedillumination state are possible. Moreover, other embodiments in whichthe headlights and/or taillights of a vehicle change from an illuminatedstate to a non-illuminated state are also possible.

FIG. 23C depicts parked vehicle 2302 (from a different angle) having anengine 2306 that is warm and tires 2308 a and 2308 b that are cool. Asused herein “warm” and “cool” may refer to deviations from expectedtemperature values that may be predetermined and/or learned. Forexample, an engine may be “warm” if it is above ambient temperature andmay be “cool” if it is at or below ambient temperature. in embodimentsof the present disclosure, a reference to an engine, or to thetemperature of the engine, may relate to a certain area of the vehiclewhose temperature is typically affected by the engine's temperature, forexample, the engine hood located at the front of the vehicle. The “warm”and “cold” temperature thresholds may be selected to reflect theexpected temperature of the engine hood under specific conditions,possibly with some margins to reduce false positive or false negativedetections, as desired. In one example, under sunny conditions, thethreshold temperature may be adjusted to account for heating from thesun after determining that the vehicle's hood is exposed to the sun). Inanother example, the effects of the sun may be factored in, taking intoaccount the color of the vehicle, which may be determined by spectralanalysis of the image. In another example, the threshold may bedetermined by averaging the temperature of, for example, a previouslydetected parked cars and/or a specific area of one or more parked cars,possibly within a local area of the car which is being presentlymonitored.

Similarly, a tire temperature may be used as an indication of thevehicle's state. For example, a tire may be “warm” if it is above roadtemperature and may be “cool” if it is at or below road temperature. Inanother example, the threshold temperature (e.g., the temperature thatis used to distinguish between “warm” and “cold” tires) may relate tothe expected or computed operating temperature of the tires. The workingtemperature computation may take into the ambient conditions, andpossibly also a model of driving and its effects over the tiretemperature. In yet another example, the working temperature computationcan also include a tire cooling model. The tire cooling model may alsotake into account ambient conditions. In the example of FIG. 23C, thesystem may, for example, use method 2400 of FIG. 24 to determine apredicted state of vehicle 2302 based on the temperatures of engine 2306and tires 2308 a and 2308 b.

FIG. 24 is a flowchart showing an exemplary process 2400 for determininga predicted state of a parked vehicle, consistent with disclosedembodiments. At step 2402, processing unit 110 may receive a pluralityof images of an environment of a host vehicle via data interface 128.For instance, cameras included in image acquisition unit 120 (such asimage capture devices 122,124, and 126 having fields of view 202, 204,and 206) may capture a plurality of images of an area forward and/or tothe side of the host vehicle and transmit them over a digital connection(e.g., USB, wireless, Bluetooth, etc.) to processing unit 110.

The plurality of images may be collected over time. For example, theplurality may include a first image captured at time t=0, a second imagecaptured at t=0.5 seconds, and a third image capture at t=1.0 seconds.The timing between the images may depend at least upon the scan rates ofthe one or more image capture devices.

At step 2404, processing unit 110 may analyze the plurality of images toidentify a parked vehicle. For example, identifying a parked vehicle mayfurther include associating at least one bounding box with a shape ofthe side of the parked vehicle. For example, the analysis may beperformed using a learned algorithm as discussed above with reference toFIGS. 9 and 10. In some embodiments, step 2404 may further includeidentifying an engine in the front of the identified target vehicleand/or a wheel on the side of the identified target vehicle. Forexample, the analysis may be performed using a learned algorithm asdiscussed above with reference to FIG. 11.

At step 2406, processing unit 110 may analyze the plurality of images toidentify a change in illumination state of the parked vehicle. :Forexample, processing unit 110 may identify taillights and/or headlightsof the parked vehicle and monitor the identified taillights and/orheadlights for a change from non-illuminated to illuminated (as seen inthe example of FIG. 23B) or from illuminated to non-illuminated. Otherembodiments may include more detailed changes, for example, fromillumination of parking lights only to illumination of brake lights orthe like.

In some embodiments, method 2400 may include determining, based on thechange in the illumination state, the predicted state of the parkedvehicle and may proceed directly to step 2412 (that is, cause at leastone navigational response by the host vehicle based on the predictedstate of the parked vehicle). For example, based on a change in theillumination state of the at least one light associated with the parkedvehicle from a non-illuminated state to an illuminated state, processingunit 110 may determine that the predicted state of the parked vehicleincludes an indication that an engine of the parked vehicle has beenstarted. Similarly, based on a change in the illumination state of theat least one light associated with the parked vehicle from anilluminated state to a non-illuminated state, processing unit 110 maydetermine that the predicted state of the parked vehicle includes anindication that an engine of the parked vehicle has been turned off.Accordingly, in such embodiments, these determinations may not involvethe use of thermal images.

At step 2408, processing unit 110 may receive at least one thermal image(that is, an infrared image) of an environment of a host vehicle viadata interface 128. For instance, cameras included in image acquisitionunit 120 (such as image capture devices 122, 124, and 126 having fieldsof view 202, 204, and 206) may capture at least one thermal image of anarea forward and/or to the side of the host vehicle and transmit themover a digital connection (e.g., USB, wireless, Bluetooth, etc.) toprocessing unit 110.

Step 2408 may be performed separately from or simultaneously with step2402. Thus, in some embodiments, the visual and infrared images may bereceived simultaneously. In other embodiments, differing scan ratesand/or differing speeds of transmission between one or more imagecapture devices and one or more infrared image captures devices mayresult in delay between the visual and infrared images.

In some embodiments, method 2400 may further include aligning at leastone of the plurality of images with the at least one thermal image.Based on the alignment, method 2400 may further include identifying atleast one of the engine area or at least one wheel component area of theparked vehicle in the aligned at least one thermal image.

At step 2410, processing unit 110 may determine a predicted state of theparked vehicle based on the analysis of step 2406 and/or analysis of theat least one thermal image. For example, a predicted state may includean indication that an engine of the parked vehicle has been started oran indication that an engine of the parked vehicle has been turned off.By way of further example, a predicated state may include an indicationthat the parked vehicle is not expected to move within a predeterminedtime period, an indication that the parked vehicle is expected to movewithin a predetermined time period, or an indication that a door of theparked vehicle is expected to open within a predetermined time period.

In some embodiments, analysis of the at least one thermal image mayinclude determining a temperature value of an engine area of the parkedvehicle. For example, if the temperature value is below a threshold,processing unit 110 may determine a predicted state including anindication that the parked vehicle is not expected to move within apredetermined time period. The predetermined time period may, forexample, depend on known characteristics of the parked vehicle or theengine thereof.

In some embodiments, analysis of the at least one thermal image mayinclude determining a first temperature value of an engine area of theparked vehicle and a second temperature value of at least one wheelcomponent of the parked vehicle. In such embodiments, the predictedstate of the parked vehicle may be determined based on a comparison ofthe first temperature value to a first threshold and a comparison of thesecond temperature value to a second threshold. For example, if thefirst temperature value exceeds the first threshold and the secondtemperature value is below the second threshold, processing unit 110 maydetermine a predicted state including an indication that the parkedvehicle is expected to move within a predetermined time period. By wayof further example, if the first temperature value exceeds the firstthreshold and the second temperature value exceeds the second threshold,processing unit 110 may determine a predicted state including anindication that a door of the parked vehicle is expected to open withina predetermined time period.

In some embodiments, processing unit 110 may perform additionalmonitoring of received images based, at least in part, on the predictedstate, whether the predicted state is based on a change in illumination,analysis of at least one thermal image, or a combination thereof. Forexample, if the predicted state indicates that an engine of the parkedvehicle has been turned off and/or indicates that a door of the parkedvehicle is expected to open within a predetermined time period,processing unit 110 may monitor one or more portions of received imagesfor a change in an image characteristic of a door edge of the parkedvehicle. An example of this monitoring is explained above with referenceto method 1400 of FIG. 14. By way of further example, if the predicatedstate indicates that an engine of the parked vehicle has been turned onand/or indicates that the parked vehicle is expected to move within apredetermined time period, processing unit 110 may monitor one or morewheel components of the received images for motion of the wheelcomponents. An example of this monitoring is explained above withreference to method 1700 of FIG. 17.

At step 2412, processing unit 110 may cause a navigational change of thehost vehicle. For example, navigational responses may include a changein a heading direction of the host vehicle (as depicted in FIG. 13above), a lane shift, a change in acceleration, and the like. Processingunit 110 may cause the one or more navigational responses based on thepredicted state determined performed at step 2410. For example,processing unit 110 may move the host vehicle away from the parkedvehicle and/or decelerate the host vehicle in response to predictedstates indicating that the parked vehicle is expected to move within apredetermined time period or indicating that a door of the parkedvehicle is expected to open within a predetermined time period.

FIG. 25 is a flowchart showing an exemplary process 2500 for aligningvisual and infrared images. At steps 2502 and 2504, processing unit 110may receive at least one visual image of an environment of a hostvehicle and at least one infrared image of the environment via datainterface 128. For instance, cameras included in image acquisition unit120 (such as image capture devices 122, 124, and 126 having fields ofview 202, 204, and 206) may capture at least one visual image and the atleast one infrared image of an area forward and/or to the side of thehost vehicle and transmit them over a digital connection (e.g., USB,wireless, Bluetooth, etc) to processing unit 110.

In some embodiments, the visual and infrared images may be receivedsimultaneously. in other embodiments, differing scan rates and/ordiffering speeds of transmission between one or more image capturedevices and one or more infrared image captures devices may result indelay between the visual and infrared images.

At step 2506, processing unit 110 may select a set of reference pointsin the at least one infrared image. :For example, the set of referencepoints may be chosen randomly or may include identification of a knownobject (e.g., a pedestrian, a tree, a vehicle, etc.) based on knowncharacteristics.

At step 2508, processing unit 110 may project the reference points fromthe at least one infrared image to the at least one visual image. Forexample, processing unit 110 may project a shape (e.g., an ellipse, arectangle, a trapezoid, etc.) representing (e.g., surrounding) thereference points onto a location in the at least one visual image.

At step 2510, processing unit 110 may optimize the gain and/or exposurefor the part of the visual image corresponding to the reference points.For example, the improved contrast resulting from the optimization mayresult in more reliable alignment. Step 2508 is optional and need not beperformed in all embodiments.

At step 2512, processing unit 110 may align the at least one infraredimage with the at least one visual image. For example, aligning (ormatching) the images may include searching along epipolar lines for adistance that optimizes an alignment measure. In this example,optimization of alignment measures may ensure that distances between thereference points and the viewer and/or between the reference points andother objects is the same in both the visual and infrared images.

Navigating Based on Detected Spacing Between Vehicles

Systems and methods that identify vehicles and space between thevehicles may allow for navigating based on the detected space.Navigating in this way may allow for preemptive monitoring for andreaction to movement in the detected spaces with a shorter reaction timethan traditional motion detection. Embodiments of the present disclosuredescribed below relate to systems and methods for navigating based ondetected spacing between vehicles.

FIG. 26 is an exemplary functional block diagram of memory 140 and/or150, which may be stored/programmed with instructions for performing oneor more operations consistent with the disclosed embodiments. Althoughthe following refers to memory 140, one of skill in the art willrecognize that instructions may be stored in memory 140 and/or 150.

As shown in FIG. 26, memory 140 may store an image analysis module 2602,a vehicle identification module 2604, a spacing calculation module 2606,and a navigational response module 2608. The disclosed embodiments arenot limited to any particular configuration of memory 140. Further,applications processor 180 and/or image processor 190 may execute theinstructions stored in any of modules 2602-2608 included in memory 140.One of skill in the art will understand that references in the followingdiscussions to processing unit 110 may refer to applications processor180 and image processor 190 individually or collectively. Accordingly,steps of any of the following processes may be performed by one or moreprocessing devices.

In one embodiment, image analysis module 2602 may store instructions(such as computer vision software) which, when executed by processingunit 110, performs image analysis of one or more images acquired by oneof image capture devices 122, 124, and 126. In some embodiments,processing unit 110 may combine information from a set of images withadditional sensory information (e.g., information from radar, lidar,etc.) to perform the image analysis. As described in connection withvehicle identification module 2604 below, image analysis module 2602 mayinclude instructions for detecting a vehicle using one or more features(e.g., a front, a rear, a side, or the like).

In one embodiment, vehicle identification module 2604 may storeinstructions (such as computer vision software) which, when executed byprocessing unit 110, performs analysis of one or more images acquired byone of image capture devices 122, 124, and 126. As described inconnection with FIGS. 9-14 above, vehicle identification module 2604 mayinclude instructions for determining bounding boxes of one or morevehicles.

In one embodiment, spacing calculation module 2606 may storeinstructions (such as computer vision software) which, when executed byprocessing unit 110, performs analysis of one or more images acquired byone of image capture devices 122, 124, and 126. As described inconnection with FIGS. 27A and 27B below, and in cooperation with vehicleidentification module 2604, spacing calculation module 2606 may includeinstructions for calculating one or more spacings between the identifiedvehicles.

In one embodiment, navigational response module 2608 may store softwareexecutable by processing unit 110 to determine a desired navigationalresponse based on data derived from execution of image analysis module2602, vehicle identification module 2604, and/or spacing calculationmodule 2606. For example, navigational response module 2608 may cause anavigational change in accordance with method 2800 of FIG. 28, describedbelow.

Furthermore, any of the modules (e.g., modules 2602, 2604, and 2606)disclosed herein may implement techniques associated with a trainedsystem (such as a neural network or a deep neural network) or anuntrained system.

FIG. 27A depicts a road 2702 from a point-of-view of a system consistentwith the disclosed embodiments. Road 2702 may include a plurality ofstationary vehicles, e.g., vehicle 2704 and vehicle 2706. As describedbelow with respect to method 2800 of FIG. 28, the system may identifybounding boxes for the sides of the stationary vehicles, e.g., sidebounding box 2708 for vehicle 2704 and side bounding box 2710 forvehicle 2706. As further described below with respect to method 2800 ofFIG. 28, the system may identify a spacing between the identifiedbounding boxes using the front of one bounding box and the rear of anadjacent bounding box, e.g., front 2712 of bounding box 2708 and rear2714 of bounding box 2710.

FIG. 27B also depicts road 2702 with vehicle 2704 and vehicle 2706 froma point-of-view of a system consistent with the disclosed embodiments.As in FIG. 27A, the system has identified side bounding box 2708 forvehicle 2704 and side bounding box 2710 for vehicle 2706. As depicted inFIG. 27B, a hot spot 2716 has been identified based on a spacingidentified between the front 2712 of bounding box 2708 and the rear 2714of bounding box 2710. As described below with respect to method 2800 ofFIG. 28, the system may determine a navigational response of a hostvehicle based on the identified hot spot.

FIG. 28 is a flowchart showing an exemplary process 2800 for navigatingbased on detected spacing between vehicles, consistent with disclosedembodiments. At step 2802, processing unit 110 may receive a pluralityof images of an environment of a host vehicle via data interface 128.For instance, cameras included in image acquisition unit 120 (such asimage capture devices 122, 124, and 126 having fields of view 202, 204,and 206) may capture at least one image of an area forward and/or to theside of the host vehicle and transmit them over a digital connection(e.g., USB, wireless, Bluetooth, etc.) to processing unit 110.Additional information from other sensors, e.g., RADAR, LIDAR, acousticsensors, or the like, may be used in combination with or in lieu of theplurality of images.

At step 2804, processing unit 110 may analyze at least one of theplurality of images to identify at least two stationary vehicles. Forexample, identifying a stationary vehicle may further includeassociating at least one bounding box with a shape of the side of thestationary vehicle. For example, the analysis may be performed using alearned algorithm as discussed above with reference to FIGS. 9 and 10.

At step 2806, processing unit 110 may determine a spacing between theidentified vehicles. For example, processing unit 110 may scan the atleast one image from left to right (or from right to left) to identify aright edge and an adjacent left edge. The identified right edge andidentified left edge may comprise the front of one bounding box and therear of another bounding box. The right edge and the left edge may forma gap pair between which the spacing may be calculated. In suchembodiments, the spacing may correspond to a distance between a front ofone of the stationary vehicles and a rear of the other stationaryvehicle.

In other embodiments, the identified right edge and identified left edgemay comprise one bounding box on one side of the road and anotherbounding box on the other side of the road. In such embodiments, thespacing may correspond to a distance between adjacent sides of thestationary vehicles.

In some embodiments, step 2806 may further include calculating adistance between the host vehicle and the determined spacing. Forexample, processing unit 110 may calculate the distance based on aheight and a focal length of an image capture device (e.g., a camera) ofthe host vehicle. Based on known characteristics (such as height) ofpedestrians or other objects, processing unit 110 may determine a shapewithin and/or near the calculated spacing as a “hot spot” for appearanceof a pedestrian or other object. For example, the shape may be arectangle, an ellipse, or other shape.

At step 2808, processing unit 110 may cause a navigational change of thehost vehicle. For example, navigational responses may include a turn (asdepicted in FIG. 13 above), a lane shift (e.g., moving the host vehicleover within a lane of travel or changing a lane of travel of the hostvehicle), a change in acceleration (e.g., slowing the host vehicle), andthe like. In some embodiments, the at least one navigational change maybe effected by actuating at least one of a steering mechanism, a brake,or an accelerator of the host vehicle.

Processing unit 110 may cause the one or more navigational responsesbased on the spacing calculated at step 2806. For example, processingunit 110 may determine that the calculated spacing is sufficient toaccommodate a pedestrian. Based on this determination, processing unitmay slow the host vehicle and/or move the host vehicle away from thespacing. In other words, the navigational change may be caused when thespacing between the two stationary vehicles is determined to besufficient for a pedestrian to traverse.

By way of further example, processing unit 110 may determine that thecalculated spacing is sufficient to accommodate a vehicle. Based on thisdetermination, processing unit may slow the host vehicle and/or move thehost vehicle away from the spacing. In other words, the navigationalchange may be caused when the spacing between the two stationaryvehicles is determined to be sufficient for a target vehicle totraverse.

By way of further example, based on monitoring of the hot spot,processing unit 110 may slow the host vehicle and/or move the hostvehicle away from the spacing when a pedestrian or other object isidentified in or near the hot spot or when motion is detected in or nearthe hot spot.

In some embodiments, then, processing unit 110 may detect, based onanalysis of the plurality if images, a pedestrian in the spacing betweenthe two stationary vehicles. For example, processing unit 110 may usethe calculated spacing and expected heights of pedestrians to determinelocations in the received images where the head of a pedestrian may beexpected to appear (e.g., hot spot 2716 of FIG. 27B).

Detection of a pedestrian may be performed using a classifier trained onpedestrians, similar to the trained classifier for vehicle sidesdiscussed above. In such an example, if a test point inside the hot spotreceives a classifier score above an upper threshold, processing unit110 may detect an approved pedestrian at that point. On the other hand,if a test point inside the hot spot receives a classifier score above alower threshold but below the upper threshold, processing unit 110 maydetect a suspect pedestrian at that point. A suspect pedestrian may befurther tracked for motion towards the road, at which point processingunit re-classifies the suspect pedestrian as an approved pedestrian.Such a method of detection may improve on traditional motion detection.

In some embodiments, at least a portion of the spacing between the twostationary vehicles may be obscured from a field of view of the camera.in such embodiments, processing unit 110 may undertake additionalanalysis in order to compensate for the obscuration.

Processing unit 110 may also use data derived from execution of velocityand acceleration module 406 to cause the one or more navigationalresponses. Multiple navigational responses may occur simultaneously, insequence, or any combination thereof.

The foregoing description has been presented for purposes ofillustration. It is not exhaustive and is not limited to the preciseforms or embodiments disclosed. Modifications and adaptations will beapparent to those skilled in the art from consideration of thespecification and practice of the disclosed embodiments. Additionally,although aspects of the disclosed embodiments are described as beingstored in memory, one skilled in the art will appreciate that theseaspects can also be stored on other types of computer readable media,such as secondary storage devices, for example, hard disks or CD ROM, orother forms of RAM or ROM, USB media, DVD, Blu-ray, 4K Ultra HD Blu-ray,or other optical drive media.

Computer programs based on the written description and disclosed methodsare within the skill of an experienced developer. The various programsor program modules can be created using any of the techniques known toone skilled in the art or can be designed in connection with existingsoftware. For example, program sections or program modules can bedesigned in or by means of .Net Framework, .Net Compact Framework (andrelated languages, such as Visual Basic, C, etc.), Java, C++,Objective-C, HTML, HTML/AJAX combinations, XML, or HTML with includedJava applets.

Moreover, while illustrative embodiments have been described herein, thescope of any and all embodiments having equivalent elements,modifications, omissions, combinations (e.g., of aspects across variousembodiments), adaptations and/or alterations as would be appreciated bythose skilled in the art based on the present disclosure. Thelimitations in the claims are to be interpreted broadly based on thelanguage employed in the claims and not limited to examples described inthe present specification or during the prosecution of the application.The examples are to be construed as non-exclusive. Furthermore, thesteps of the disclosed methods may be modified in any manner, includingby reordering steps and/or inserting or deleting steps. It is intended,therefore, that the specification and examples be considered asillustrative only, with a true scope and spirit being indicated by thefollowing claims and their full scope of equivalents.

What is claimed is:
 1. A system for determining a predicted state of aparked vehicle in an environment of a host vehicle, the systemcomprising: an image capture device; an infrared image capture device;and at least one processing device programmed to: receive, from theimage capture device, a plurality of images associated with theenvironment of the host vehicle; analyze at least one of the pluralityof images to identify the parked vehicle; analyze at least two of theplurality of images to identify a change in an illumination state of atleast one light associated with the parked vehicle; receive, from theinfrared image capture device, at least one thermal image of the parkedvehicle; determine, based on the change in the illumination state andanalysis of the at least one thermal image, the predicted state of theparked vehicle; and cause at least one navigational response by the hostvehicle based on the predicted state of the parked vehicle.
 2. Thesystem of claim 1, wherein the change in the illumination state of theat least one light associated with the parked vehicle includes a changefrom a non-illuminated state to an illuminated state.
 3. The system ofclaim 2, wherein the predicted state of the parked vehicle includes anindication that an engine of the parked vehicle has been started.
 4. Thesystem of claim 1, wherein the change in the illumination state of theat least one light associated with the parked vehicle includes a changefrom an illuminated state to a non-illuminated state.
 5. The system ofclaim 4, wherein the predicted state of the parked vehicle includes anindication that an engine of the parked vehicle has been turned off. 6.The system of claim 1, wherein the analysis of the at least one thermalimage includes determining a temperature value of an engine area of theparked vehicle.
 7. The system of claim 6, wherein the predicted state ofthe parked vehicle is determined based at least in part on a comparisonof the temperature value to a threshold.
 8. The system of claim 7,wherein, when temperature value is lower than a threshold value, thepredicted state of the parked vehicle includes an indication that theparked vehicle is not expected to move within a predetermined timeperiod.
 9. The system of claim 1, wherein the analysis of the at leastone thermal image includes determining a first temperature value of anengine area of the parked vehicle and a second temperature value of atleast one wheel component of the parked vehicle.
 10. The system of claim9, wherein the predicted state of the parked vehicle is determined basedon a comparison of the first temperature value to a first threshold anda comparison of the second temperature value to a second threshold. 11.The system of claim 9, wherein, when the first temperature value exceedsa first threshold and the second temperature value is lower than asecond threshold, the predicted state of the parked vehicle includes anindication that the parked vehicle is expected to move within apredetermined time period.
 12. The system of claim 9, wherein, when thefirst temperature value exceeds a first threshold and the secondtemperature value exceeds a second threshold, the predicted state of theparked vehicle includes an indication that a door of the parked vehicleis expected to open within a predetermined time period.
 13. The systemof claim 1, wherein the predicted state of the parked vehicle includesat least one of: an indication that the parked vehicle is not expectedto move within a predetermined time period, an indication that theparked vehicle is expected to move within a predetermined time period,or an indication that a door of the parked vehicle is expected to openwithin a predetermined time period.
 14. The system of claim 1, whereinthe at least one processing device is further programmed to: align atleast one of the plurality of images with the at least one thermalimage; and identify at least one of the engine area or at least onewheel component area of the parked vehicle in the aligned at least onethermal image.
 15. A method for determining a predicted state of aparked vehicle in an environment of a host vehicle, the methodcomprising: receiving, from an image capture device, a plurality ofimages associated with the environment of the host vehicle; analyzing atleast one of the plurality of images to identify the parked vehicle;analyzing at least two of the plurality of images to identify a changein an illumination state of at least one light associated with theparked vehicle; receiving, from an infrared image capture device, atleast one thermal image of the parked vehicle; determining, based on thechange in the illumination state and analysis of the at least onethermal image, the predicted state of the parked vehicle; and causing atleast one navigational response by the host vehicle based on thepredicted state of the parked vehicle.
 16. The method of claim 15,wherein: the analysis of the at least one thermal image includesdetermining a temperature value of an engine area of the parked vehicle,and when the temperature value is lower than a threshold value, thepredicted state of the parked vehicle includes an indication that theparked vehicle is not expected to move within a predetermined timeperiod.
 17. The method of claim 15, wherein: the analysis of the atleast one thermal image includes determining a first temperature valueof an engine area of the parked vehicle and a second temperature valueof at least one wheel component of the parked vehicle, and when thefirst temperature value exceeds a first threshold and the secondtemperature value is lower than a second threshold, the predicted stateof the parked vehicle includes an indication that the parked vehicle isexpected to move within a predetermined time period.
 18. The method ofclaim 15, wherein: the analysis of the at least one thermal imageincludes determining a first temperature value of an engine area of theparked vehicle and a second temperature value of at least one wheelcomponent of the parked vehicle, and when the first temperature valueexceeds a first threshold and the second temperature value exceeds asecond threshold, the predicted state of the parked vehicle includes anindication that a door of the parked vehicle is expected to open withina predetermined time period.
 19. The method of claim 15, furthercomprising: aligning at least of the plurality of images with the atleast one thermal image; and identifying at least one of the engine areaor at least one wheel component area of the parked vehicle in thealigned at least one thermal image.
 20. A non-transitorycomputer-readable medium storing instructions, which, when executed byat least one processing device, perform a method comprising: receiving,from an image capture device, a plurality of images associated with anenvironment of a host vehicle; analyzing at least one of the pluralityof images to identify a parked vehicle; analyzing at least two of theplurality of images to identify a change in an illumination state of atleast one light associated with the parked vehicle; receiving, from aninfrared image capture device, at least one thermal image of the parkedvehicle; determining, based on the change in the illumination state andanalysis of the at least one thermal image, the predicted state of theparked vehicle; and causing at least one navigational response by thehost vehicle based on the predicted state of the parked vehicle.
 21. Asystem for determining a predicted state of a parked vehicle in anenvironment of a host vehicle, the system comprising: an image capturedevice; at least one processing device programmed to: receive, from theimage capture device, a plurality of images associated with theenvironment of the host vehicle; analyze at least one of the pluralityof images to identify the parked vehicle; analyze at least two of theplurality of images to identify a change in an illumination state of atleast one light associated with the parked vehicle; determine, based onthe change in the illumination state, the predicted state of the parkedvehicle; and cause at least one navigational response by the hostvehicle based on the predicted state of the parked vehicle.
 22. Thesystem of claim 21, wherein the change in the illumination state of theat least one light associated with the parked vehicle includes a changefrom a non-illuminated state to an illuminated state.
 23. The system ofclaim 22, wherein the predicted state of the parked vehicle includes anindication that an engine of the parked vehicle has been started. 24.The system of claim 21, wherein the change in the illumination state ofthe at least one light associated with the parked vehicle includes achange from an illuminated state to a non-illuminated state.
 25. Thesystem of claim 24, wherein the predicted state of the parked vehicleincludes an indication that an engine of the parked vehicle has beenturned off.